High-priority Areas Research Articles

A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods

Hazardous flooding occurs across most climate zones. Owing to the lack of appropriate infrastructures and applicable predictive methods, flooding in arid and semi-arid regions may be especially damaging. Based on a study of Abarkuh County, Iran, we introduce an integrated approach for identifying high-priority flood risk areas using machine learning (ML) and multi-criteria decision-making (MCDM) methods, which is transferable to other (semi)arid regions. Results indicate that among the ML models we examined—including classification and regression tree (CART), mixture discriminant analysis (MDA), and support vector machine (SVM)—the SVM model performs best. We estimate that 75% of the study area is subject to high or very flood hazard. Our application of the Jackknife technique identifies precipitation, vegetation, and drainage density as the most important conditional factors for regional flood hazards. Our analytical network process (ANP)-decision making trial and evaluation laboratory (DEMATEL) results reveal that population density and agricultural area density have the greatest influence on flood vulnerability. Results integrating SVM and ANP-DEMATEL flood hazard and vulnerability maps indicate that 6% of the study area is at high or very high flood risk. Application of this approach can assist local authorities in identifying priority areas for flood management interventions.

Estimation of Cropland Nitrogen Runoff Loss Loads in the Yangtze River Basin Based on the Machine Learning Approaches

A quantitative understanding of cropland nitrogen (N) runoff loss is critical for developing efficient N pollution control strategies. Using correlation analysis, a structural equation model, variance decomposition, and machine learning methods, this study identified the primary influencing factors of total N (TN) runoff loss from uplands (n=570) and paddy (n=434) fields in the Yangtze River Basin (YRB) and then developed a machine learning-based prediction model to quantify cropland N runoff loss load. The results indicated that runoff depth, soil N content, and fertilizer addition rate were the major influencing factors of TN runoff loss from uplands, whereas TN runoff loss rate from paddy fields was mainly regulated by runoff depth and fertilizer addition rate. Among the four used machine learning methods, the prediction models based on the random forest algorithm presented the highest accuracy (R2=0.65-0.94) for predicting upland and paddy field TN runoff loss rates. The random forest algorithm based model estimated a total cropland TN loss load in the YRB of 0.47 Tg·a-1 (upland:0.25 Tg·a-1; paddy field:0.22 Tg·a-1) in 2013, with 58% of TN runoff loss load derived from the midstream and downstream regions. The models predicted that TN runoff loss loads from croplands in YRB would decrease by 2.4%-9.3% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. To mitigate cropland N nonpoint source pollution in YRB, it is essential to integrate efficient water, fertilizer, and soil nutrient managements as well as to consider the midstream and downstream regions as the high priority area. The machine learning-based modeling method developed in this study overcame the difficulty of identifying the functional relationships between cropland TN loss rate and multiple influencing factors in developing relevant prediction models, providing a reliable method for estimating regional and watershed cropland TN loss load.

Assessing wildfire activity and forest loss in protected areas of the Amazon basin

Wildfires annually burn extensive areas in the Amazonia. Still, more is needed to know about the complex combination of triggering socioeconomic factors and environmental policies that motivate and explain the growing wildfire activity and forest losses. This study assessed wildfire occurrence, exposure, and transmission to natural forests in protected natural areas (n = 438) from 9 countries encompassing 140 million hectares of the Amazon basin. The fire ignitions and burned areas were first detected using remote sensing products from 2001 to 2018. We then conducted a wildfire transmission analysis to determine the neighboring fire source hotspots and land uses associated with human-caused ignitions encroaching on protected areas. Finally, we analyzed the environmental policies to understand the failure of such regulations to prevent the escape of fires burning large areas within protected sites. The results showed that recurrent wildfire hotspots cover a reduced portion but account for the bulk of the burned area. We found that, on average, wildfires burned 86,700 ha yr−1 of natural forests in protected sites, of which 10.5% corresponded to large incoming fires. Savanna and grasslands concentrated 29% and 41% of fire ignitions, where farmers and ranchers use fire periodically to clear extensive grazing properties. However, this high ignition density did not necessarily correlate with burned areas. The lack of a clear understanding of environmental policies and insufficient law enforcement was likely the main reasons for the continuous wildfire transmission to protected forests. The quantitative outcomes provide science-based criteria for detecting the high-priority areas within the Amazon where management efforts could help reverse the growing wildfire risk to protected natural forests.

A survey of weed research priorities: key findings and future directions

AbstractWe conducted an online survey of weed scientists in the United States and Canada to (1) identify research topics perceived to be important for advancing weed science in the next 5 to 10 years and (2) gain insight into potential gaps in current expertise and funding sources needed to address those priorities. Respondents were asked to prioritize nine broad research areas, as well as 5 to 10 subcategories within each of the broad areas. We received 475 responses, with the majority affiliated with academic institutions (55%) and working in cash crop (agronomic or horticultural) study systems (69%). Results from this survey provide valuable discussion points for policy makers, funding agencies, and academic institutions when allocating resources for weed science research. Notably, our survey reveals a strong prioritization of Cultural and Preventative Weed Management (CPWM) as well as the emerging area of Precision Weed Management and Robotics (PWMR). Although Herbicides remain a high-priority research area, continuing challenges necessitating integrated, nonchemical tactics (e.g., herbicide resistance) and emerging opportunities (e.g., robotics) are reflected in our survey results. Despite previous calls for greater understanding and application of weed biology and ecology in weed research, as well as recent calls for greater integration of social science perspectives to address weed management challenges, these areas were ranked considerably lower than those focused more directly on weed management. Our survey also identified a potential mismatch between research priorities and expertise in several areas, including CPWM, PWMR, and Weed Genomics, suggesting that these topics should be prime targets for expanded training and collaboration. Finally, our survey suggests an increasing reliance on private sector funding for research, raising concerns about our discipline’s capacity to address important research priority areas that lack clear private sector incentives for investment.

Zero-Trust for the System Design Lifecycle

Abstract In an age of worsening global threat landscape and accelerating uncertainty, the design and manufacture of systems must increase resilience and robustness across both the system itself and the entire systems design process. We generally trust our colleagues after initial clearance/background checks; and systems to function as intended and within operating parameters after safety engineering review, verification, validation, and/or system qualification testing. This approach has led to increased insider threat impacts; thus, we suggest moving to the “trust, but verify” approach embodied by the Zero-Trust paradigm. Zero-Trust is increasingly adopted for network security but has not seen wide adoption in systems design and operation. Achieving the goal of Zero-Trust throughout the systems lifecycle will help to ensure that no single bad actor—whether human or machine learning/artificial intelligence (ML/AI)—can induce failure anywhere in a system’s lifecycle. Additionally, while ML/AI and their associated risks are already entrenched within the operations phase of many systems’ lifecycles, ML/AI is gaining traction during the design phase. For example, generative design algorithms are increasingly popular, but there is less understanding of potential risks. Adopting the Zero-Trust philosophy helps ensure robust and resilient design, manufacture, operations, maintenance, upgrade, and disposal of systems. We outline the rewards and challenges of implementing Zero-Trust and propose the framework for Zero-Trust for the system design lifecycle. This article highlights several areas of ongoing research with focus on high priority areas where the community should focus efforts.

Towards a More Efficient In and Ex Situ Conservation of Sri Lankan Wild Rice Species.

Five species of wild Oryza (O. nivara, O. rufipogon, O. eichengeri, O. rhizomatis and O. granulata), including the endemic species O. rhizomatis, have been recorded in Sri Lanka. These species are facing continuous decline in their populations due to natural and anthropogenic processes, with habitat loss being the main threat. This study aimed to provide information on the distribution, the current status of ex situ and in situ conservation, and to identify high-priority species and sites of wild rice in Sri Lanka, in order to improve the effectiveness of conservation efforts. Occurrence records of Sri Lankan wild rice species were collected from literature, gene banks, and field surveys. The distribution of these species was mapped, and areas with high species richness were identified. A gap analysis was conducted to determine the high-priority areas and species for ex situ and in situ conservation. It was found that about 23% of the wild rice populations in Sri Lanka were within protected areas, and by expanding these protected areas by 1 km, an additional 22% of the populations located on the border of these areas could be effectively conserved. Our analysis also revealed that 62% of Sri Lankan wild rice populations were not represented in gene banks. The species-rich areas were found to be in only two districts (Polonnaruwa and Monaragala), and less than 50% of these areas were within protected areas. Based on these findings, O. rhizomatis, O. eichengeri, and O. rufipogon were identified as high-priority species for in situ conservation. Ex situ collections were also deemed necessary for O. granulata and O. rhizomatis to ensure diversity representation in gene banks.

Simulating with a combination of RUSLE GIS and sediment delivery ratio for soil restoration.

Erosion by water is the main cause of land degradation. Landscapes degraded by erosion need to be restored in many respects, and particularly in terms of ecosystem services. From an economic and management perspective, care is needed to select priority areas and determine the means to be applied to restore them. Globally, the model most commonly used to produce scenarios for the prevention of soil losses is the Revised Universal Soil Loss Equation (RUSLE). This study of the subbasin of the Sulakyurt Dam Basin in Turkey aims (1) to identify the distribution of soil losses over time and by location, and (2) to grade the priority areas for the prevention of soil losses by means of a simulation. The average potential soil losses in the area under study are estimated at 42.35 t ha-1year-1, and the average actual losses at 39.49 t ha-1year-1. According to the simulation, 27.61% of the study area (2782ha) is of the highest priority for soil restoration. In our study, forests have the highest soil losses, which is contrary to the natural protection that forests provide against erosion. The high rates are due to the slope, the forest area is very steep. So it is the slope factor that outweighs the vegetation cover factor. Of the forest areas, 41.74% (1766ha) falls within the areas of highest priority. The study serves as a guide for landscape planning and the determination of erosion risk in restoration efforts, and for identifying the methods to be adopted during the restoration work to reduce the loss of soil.

Combined effects of dry-wet irrigation, redox changes and microbial diversity on soil nutrient bioavailability in the rice field

Sustainable development goals (SDGs) by United Nations are some of the high-priority areas of research and this article established the sustainable agronomic practices converging soil chemistry, crop productivity and soil microbial involvement in nutrient modulation. Alternate wetting-drying (AWD) cultivation with implications on soil microbiome, nutritional dynamics and rice yield during pre-monsoon (boro) and monsoon (aman) season are not well studied. In the present 4-year field study, the impact of AWD irrigation in pre-monsoon and flooded irrigation in monsoon on soil microbiota and the nutrient pool has been studied. Nutrient-less pond water has been used to avoid any external nutrient input from irrigation water to ensure the sole elemental flux within the soil itself. The release of soil nutrients into the soil-aqueous system, influencing microbial populations and modulating the redox status was explored. Results indicated an increase in total concentration as well as bioavailability of selected nutritional elements (N, P, K, Fe, Ca, Mg, Cu and Zn) by 16–54% in the pre-monsoon cultivation relative to monsoon cultivation. Three plant growth phases (developing, milking and harvest) were considered to check the nutrient modulations in soil and plant tissues along with the plant growth and elemental uptake continuum. Crop plant measurements were improved and grain yields were found to be 5.2–6.46% increased under AWD and microbial activity. Krona charts, relative abundance, rarefaction curve and multivariate analysis of metagenomics data showed that the pre-monsoon soil was more enriched and maintained a balance between soil pH and microbial biomass than the monsoon soil. Microbial community diversity associated with plant growth phases was also found to be different depending on the seasonal alterations. Bacillus sp., Acidothiobacillus sp., Pseudomonas sp., Rhizobium sp., Burkholderia sp. were predominant in pre-monsoon soil releasing pulses of N, P, K, Ca and Mg whereas Verrucomicrobia was found to be dominant in monsoon soil where Fe was released. This study is a first of its kind that showed the combined effect of season and some specific groups of soil microbes on macro-micro nutritional availability in soil and enhanced plant quality.

Critical areas for pollinator conservation in Mexico: A cross-border priority

Pollinators are currently the focus of international concern, as numerous studies have documented declines in their diversity and abundance. Pollinator conservation has therefore become an international priority. We selected 815 pollinator species (including bats, bees, hummingbirds, and hawkmoths) inhabiting the southern United States, Mexico and part of Central America to assess the overlap between their distributions and land-use changes. We also analyzed the representativeness values of species contained within current protected areas (PAs). Then, we considered three important pressures —habitat loss, population density and infrastructure, and crop-specific pesticides— to determine high-priority and complementary conservation areas to optimize the long-term protection of these species. We found strong overlap (74.5 %) between the distributions of pollinator species and areas with a moderate or high degree of human modification. Furthermore, current PAs are both insufficient and inefficient: they cover only 14.3 % of the study area and represent on average 14.6 ± 8.6 % of the species' total distribution area. For 62.7 % of the species, <15 % of their distribution occurred within PAs. This picture is particularly alarming for the most range-restricted and threatened species. If our prioritization were implemented, the protection coverage would increase to 30 % (in line with the post-2020 Global Biodiversity Framework), substantially increase representativeness values, and cover over 25 % of the ranges of all species. In the context of the current biodiversity crisis, we provide insights about where transboundary efforts should focus to design a PA network that is effective for the conservation of whole biodiversity, including important ecosystem processes.

A Framework to Identify Priority Areas for Restoration: Integrating Human Demand and Ecosystem Services in Dongting Lake Eco-Economic Zone, China

The identification of priority restoration areas (PRAs) for ecosystems is a critical step in establishing restoration programs. Because the majority of existing studies focused on improving the ecosystem supply, the PRAs selected are likely to be remote from human demand, and the restoration benefits will not flow to humans. To fill this gap, we constructed an improved framework integrating the ecological restoration projects’ cost and benefits as indicators for choosing PRAs. Then, we identified PRAs for each ecosystem service (ES) with Marxan, and ranked the restoration priority grades according to the superimposed value of PRAs for each ES. Finally, we adjusted the restoration priority grades based on human demand and the concentration of those areas, and chose PRAs with a high ES supply and demand. This framework was applied to the Dongting Lake Eco-Economic Zone, one of China’s most significant ecological restoration project sites. The results indicated that the areas with “high”-, “sub-high”-, and “low”-grade PRAs, based only on the increase in the ES supply, were equal to 82, 410, and 1696 km2, respectively. After considering human demand, the PRAs moved continuously towards places with a high human demand; high-priority areas grew to reach 144 km2, while low-priority areas decreased to 1498 km2. The upgrade of the proposed framework for the identification of PRAs can contribute to increasing human well-being, while also serving as a support tool for environmental restoration management.

Exploiting Service Design in Service Quality: Escorting the Customer’s Experiential Value in the Journey of a Star-Rated Hotel

The present research presented a novel model for incorporating service design to create guest experiential value. Taking a star hotel in Taiwan as an example, this study used the KJ method to classify online reviews about hotel services and then created a customer journey map (CJM) through participant observation of the entire service process. By deconstructing the service process and identifying key service touchpoints, service design ideas were incorporated into the CJM. A Kano questionnaire was then adopted to accommodate 38 service scape attributes in 4 segments: 1 attribute in the maintenance area, 10 of the high-priority area, 11 of the low-priority area, and 16 of differentiation. The overall research results propose experience design strategies for the hotel industry in terms of the service content and operation processes of star-rated hotels, which can then be adjusted according to their operating conditions to construct their unique service experience content. This is expected to be the basis for subsequent research and serve as a basis and reference for improving hotel service design strategies and competitiveness in the future.

A multispecies corridor in a fragmented landscape: Evaluating effectiveness and identifying high-priority target areas.

While Misiones, Argentina contains one of the largest remnants of Upper Paraná Atlantic Forest ecoregion, one of the world's biodiversity hotspots, only ~50% of this native forest is protected. Each protected area is at risk of becoming an island of native forest surrounded by a matrix of altered habitats due to ongoing land conversion. In an effort to maximize long-term connectivity between existing protected areas, DeMatteo [1] used a multifaceted cost analysis to determine the optimal location for the region's first multispecies corridor using noninvasive data on jaguars (Panthera onca), pumas (Puma concolor), ocelots (Leopardus pardalis), southern tiger cats (Leopardus guttulus), and bush dogs (Speothos venaticus). This work builds on this framework by integrating new field data that broadens the scope of species-specific data across the region's heterogeneous landscape, which varies in vegetation, disturbance, human proximity, and protective status. In addition, two different land use layers are compared across the distributions of the five carnivores, the overlap in their independent distributions, and their relationship to the multispecies corridor. Interpretation of these land use data to species-specific habitat suitability goes beyond DeMatteo [1], with a subdivision of suitability into marginal and optimal areas. This refined scale allows a reanalysis of key areas in the multispecies corridor, where connectivity was previously defined as at highly-at-risk, allowing for a more directed development of management strategies. These analyses and their interpretation extend beyond northern-central Misiones, as the threats are not unique to this region. The need to develop management strategies that balance human-wildlife needs will continue to grow as humans expand their footprint. The techniques applied in this analysis provide a way to identify key areas that require specific management strategies, either through restoration, protection, or a combination of both.

Integration of LCSA and GIS-based MCDM for sustainable landfill site selection: a case study.

The paper aims at developing a framework for decision-support to select a sustainable landfill site in Bardaskan City (Iran) by combining life cycle sustainability assessment (LCSA) concepts and geographic information system (GIS)-based multi-criteria decision-making (MCDM). Overall, 13 criteria were chosen (three constraints and 10 factors) and classified into three main aspects of sustainability (i.e., environmental, social, and economic) to achieve the research goals. Boolean and fuzzy logic were employed to standardize the classified constraints and factors, respectively. Analytic hierarchy process (AHP) was used to calculate the factors' weights and then suitability maps were produced using the GIS analysis. The layers were combined using simple additive weighting (SAW). Next, the most sustainable sites were obtained. The results indicated that distance from city backline, groundwater depth, and distance from rural areas were the most significant factors with the weight of 0.338, 0.141, and 0.129, respectively. The final map of suitable sites was created by classifying the SAW layer according to 75, 80, and 85% of suitability to show the high, medium, and low priority areas for landfill site selection, respectively. Therefore, integration of LCSA and GIS-based MCDM to select the sustainable landfill site for municipal solid waste (MSW) is highly important, which can be effectively employed in regional and urban planning to select the location of appropriate and sustainable landfills.

Development of a protocol to identify land function based on multifunctionality and suitability analysis: A case study of the Nanyuntai Forest Farm, China

Accurately identifying regional land functions is important for balancing and coordinating multiple ecosystem services. Identification of land functions should be guided by the ecological suitability analysis framework, highlighting the primary ecological function and considering the temporal and spatial trends of land functions to resolve the contradictions among various needs of land ecology, production, and life. Taking Nanyuntai Forest Farm in Lianyungang City, Jiangsu Province, as a case study, we used the Analytical Hierarchical Process to construct a systematic and complete land multi-function evaluation and spatial suitability evaluation system, and used the ArcGIS spatial analysis technology to analyze its land function changes from 2011 to 2021. Finally, the results of multifunctionality and suitability analysis were integrated to identify the land's ecological, production, and living functions by constructing the algorithm using Python. Our results showed that the proportions of ecological, production, and living functions of the land in the study area were 56.82, 34.03, and 9.15%, respectively. This study highlighted that the results of land function identification based on comprehensive land-change-trend and spatial suitability analyses were more precise and coordinated. This comprehensive evaluation is a valuable and practical method for decision-makers and land managers to create land use maps and determine high-priority areas that require conservation measures for sustainable management. Moreover, this approach can be practically applied to other planning units.

Should I Travel by Metro? Analyzing the Service Quality Perception of Elderly and Physically Disabled Passengers in Delhi, India

Transit officials worldwide continuously learn from passenger perception surveys to improve service quality and enhance transit ridership. However, many elderly and physically disabled (E&amp;PD) passengers are transit users with special needs whose service requirements have received insufficient attention in the past. In fact, providing satisfactory transit services to E&amp;PD passengers is often a critical challenge for transit officials, since a theory of interrelations among service factors is not yet established from E&amp;PD passengers’ perspectives. Therefore, the present study aims to establish interrelations among service quality factors and identify service improvements for E&amp;PD passengers using satisfaction data from 254 E&amp;PD passengers of Delhi Metro. For this purpose, this study has employed an integrated Bayesian networks and partial least squares path modeling approach. The obtained results have shown some remarkable and meaningful interpretations. For instance, a one-unit increase in satisfaction with the “safety &amp; security” factor would induce a 68% increase in their satisfaction with overall service quality. Besides, importance–performance map analysis showed that Delhi Metro must consider “safety and security,”“passenger ease,” and “seamless connectivity” as high-priority areas needing improvements to promote E&amp;PD-friendly transit services. Overall, the findings of this study suggest several policy measures that would enrich the service quality and achieve universal design standards in metro rail transit systems.

Utilising the dental workforce in England, United Kingdom, to optimise the response to COVID-19

COVID-19 presents a unique and significant challenge to healthcare systems across the globe. Dental workforce redeployment, in England, during the response to Coronavirus (COVID-19), is the first reported national effort to redeploy a professional body into new clinical environments. The policy decision to facilitate redeployment of the dental workforce, in March 2020, by the Office of the Chief Dental Officer (OCDO), increased flexibility within workforce systems and allowed increasing demand on healthcare services to be managed safely and effectively. This paper outlines how this policy change was achieved via a multi-professional approach, mapping competencies of the dental workforce to high-priority areas of healthcare need. The dental workforce has a varied and often specialised skill set, offering expertise in infection prevention and control, airway management and often, behaviour management. These skills can be an important contribution to tackling a pandemic where expertise in these areas is vital. This increase in workforce supply allows healthcare systems to improve their surge response capabilities. Additionally, redeployment presents an opportunity to create greater and sustained collaboration between the medical and dental professions, leading to greater understanding of the contribution of oral health to wider medical wellbeing.

Adverse health events and recommended health research priorities in agility dogs as reported by dog owners.

To understand relative frequency of adverse health events, defined as injuries or infectious diseases, in dogs participating in agility and to determine health research priorities of agility dog owners. An internet-based questionnaire distributed to agility dog owners included items related to experiences with infectious diseases and injuries in agility dogs, reasons for retirement of dogs from competition, and ranking of health research priorities. Frequencies of infectious diseases in US geographic regions were compared with Chi-square tests. Research priority rankings were determined as median and interquartile range (IQR) for each topic. Rank-based tests (Kruskal Wallis and Mann-Whitney) compared rankings between participants in different agility organizations, between veterinarian and non-veterinarian competitors, and between respondents who had competed in national championship events and other respondents. There were 1,322 respondents who had competed in canine agility in the previous 6 months, with those respondents reporting a median time competing in the sport of 13 years (IQR = 8-20 years); 50% of respondents had competed in at least one national championship agility event in the preceding 5 years. Overall, 1,015 respondents (77%) indicated that one or more of their dogs had been injured and approximately one-third (n = 477, 36%) indicated that one or more dogs had likely acquired one or more infectious diseases as a result of agility activities. Specific types of infectious diseases acquired varied by geographic region in the US. Research priority rankings were similar regardless of preferred agility organization or respondent experience. The highest-ranking research topics were identification of risk factors for specific types of injuries, improvements in equipment and understanding of safe course design, and physical conditioning programs to prevent injury. Agility competitors prioritize research in areas that advance understanding of injury prevention in their dogs. Research priorities are nearly uniform among competitors regardless of their preferred agility organization or level of experience, providing a strong rationale for agility organizations to collaborate in research initiatives that improve safety and well-being for dogs competing in the sport. There has been little published research focusing on the high-priority research areas identified by competitors.

Risk Assessment and Prediction of Soil Water Erosion on the Middle Northern Slope of Tianshan Mountain

Soil erosion is a significant form of land degradation worldwide, leading to ecological degradation and a decline in agricultural productivity. The middle section of the northern slopes of Tianshan Mountain (MNSTM) in northwestern China is a high-priority area for soil water erosion prevention, and soil water erosion is a serious problem in the region. Despite this, there is a lack of research on soil water erosion in the MNSTM, and the trends and priority risk areas of soil water erosion remain unclear. Therefore, this study used the Revised Universal Soil Loss Equation (RUSLE) to quantitatively assess soil water erosion from 2001–2020 and predict it from 2030–2050. The study also used the Geodetector method to analyse the influencing factors of soil water erosion in the region. The results show that soil water erosion in the MNSTM has a fluctuating upward trend, increasing at a rate of 0.26 t hm−2 y−1 over the period 2001–2020 and reaching a maximum value of 39.08 t hm−2 in 2020. However, soil water erosion in the region is mitigated under both RCP2.6 and RCP4.5 climate scenarios. Vegetation was found to have the highest degree of influence on soil erosion, indicating that its protection and management should be prioritised for future soil and water conservation efforts. The eastern part of the MNSTM was identified as the most vulnerable area to soil and water erosion, and in the context of global climate change, it is crucial to enhance the ecological restoration of the MNSTM to reduce the risk of soil water erosion. These findings can serve as valuable information for decision makers to develop effective strategies to prevent soil erosion and improve the ecological environment in the MNSTM.

Building a thriving academic hospitalist workforce: A rapid qualitative analysis identifying key areas of focus in the field.

The hospitalist workforce has been at the forefront of the pandemic and has been stretched in both clinical and nonclinical domains. We aimed to understand current and future workforce concerns, as well as strategies to cultivate a thriving hospital medicine workforce. We conducted qualitative, semistructured focus groups with practicing hospitalists via video conferencing (Zoom). Utilizing components from the Brainwriting Premortem Approach, attendees were split into small focus groups and listed their thoughts about workforce issues that hospitalists may encounter in the next 3 years, identifying the highest priority workforce issues for the hospital medicine community. Each small group discussed the most pressing workforce issues. These ideas were then shared across the entire group and ranked. We used rapid qualitative analysis to guide a structured exploration of themes and subthemes. Five focus groups were held with 18 participants from 13 academic institutions. We identified five key areas: (1) support for workforce wellness; (2) staffing and pipeline development to maintain an adequate workforce to match clinical growth; (3) scope of work, including how hospitalist work is defined and whether the clinical skillset should be expanded; (4) commitment to the academic mission in the setting of rapid and unpredictable clinical growth; and (5) alignment between the duties of hospitalists and resources of hospitals. Hospitalists voiced numerous concerns about the future of our workforce. Several domains were identified as high-priority areas of focus to address current and future challenges.

Perbandingan Algoritma K-Means dan K-Medoids Untuk Pemetaan Daerah Penanganan Diare Pada Balita di Kabupaten Kuningan

Diarrhea is an endemic disease that contributes to the high mortality rate in Indonesia, especially among children under five. The Kuningan District Health Office had difficulties in monitoring and supervising the spread of diarrheal diseases. This study aims to produce a mapping scheme of priority areas in handling the prevention and control of the spread of diarrheal disease in children under five in Kuningan Regency. The method used is the Data Mining Clustering method by comparing two algorithms, namely the K-Means algorithm and the K-Medoids algorithm. Determination of the optimum number of clusters using the Elbow and Silhouette Coefficient methods. With this method, the result is that in the K-Means algorithm the optimum number of clusters is 3 clusters while the K-Medoids algorithm is 2 clusters. The best cluster evaluation uses the Davies-Bouldin Index (DBI) method and the results show that the K-Means DBI value is always smaller than the K-Medoids algorithm in either 2 clusters or 3 clusters, this shows that the K-Means algorithm is better than the K-Medoids algorithm. Based on these results, it is recommended to map priority areas for handling diarrheal diseases using the K-Means algorithm with 3 clusters, namely medium priority areas consisting of 9 regions, high priority areas consisting of 3 regions and low priority areas consisting of 25 regions. The results of the mapping can be used as input for the Kuningan District Health Office to develop strategies for preventing and preventing diarrheal diseases in children under five.