Extensive Monitoring Research Articles

Resilient Stormwater Management of a Coastal Catchment

Designing stormwater drainage systems considering deep uncertainty is a task that has no correct solution, rather, it can only be addressed by managing the system in a smart, robust way. Over recent decades, robust decision-making has been promoted as a solution to planning systems that are vulnerable to deep uncertainty. In this paper, we adopt a robust decision-making methodology to propose a stormwater drainage system in a coastal catchment in an arid region which is vulnerable to sea-level rise and increased precipitation intensity. We used bias-corrected precipitation and sea-level rise projections from a regional climate model, in addition to analyzing observed data. The decision-making methodology adopted is the Dynamic Adaptive Policy Pathways (DAPP). It involves building a decision tree with probable actions to consider when the stormwater system is expected to fail. The success of DAPP relies on continuous and extensive monitoring of the system and all components/factors that form risk or add to the vulnerability of the system, in addition to extensive simulations of the pre-identified actions that enable quick implementation of the solutions preceding failure of the system. This gives an early warning and aids the proactive execution of actions, hence making the system resilient to deep uncertainty. The DAPP for the study site is presented, and the advantages of relying on robust decision-making for arid regions are discussed.

B2B Customer Engagement Customer Behaviour Forecast Application

The paper presents the "Casper Customer Behavior Forecast Application" a creative project focused on the creation of an advanced decision support software system. The purpose of this system is to closely observe, fully analyze, and precisely predict the behaviors of Casper Business-to-Business (B2B) clients. Our research study implements an innovative methodology by focusing on the enhancement of client interactions through the utilization of RESTful services. Our objective is to reimagine the future of customer relationship management by analyzing and forecasting client behavior in the business-to-business (B2B) setting. The project involves a comprehensive methodology that incorporates extensive study, complex software design, and careful data analysis. In more detail, it involves extensive monitoring of consecutive RESTful interactions carried out by business-to-business consumers across a period of time. By leveraging the capabilities of modern machine learning and deep learning algorithms, our objective is to develop prediction models that establish novel benchmarks within the industry. The methodology employed in this study includes the development of labeled behavioral datasets and the utilization of a supervised machine learning framework. The evaluation of model performance will be performed systematically using a range of metrics, such as F-Score and Accuracy, in order to establish the model's robustness and reliability in making accurate predictions. The expected outcomes of this project have the potential to bring about significant changes. Primarily, the integration of machine learning and deep learning algorithms will provide our company with an important amount of knowledge. Our organization will obtain an innovative software solution that possesses the capability to precisely forecast the future actions of B2B customers. This results in enhanced inventory management and a significant reduction in client waiting periods, resulting in increased levels of customer satisfaction. Additionally, this study has the potential to make significant additions to the global academic body of knowledge in the domains of machine learning and deep learning. In simple terms, the project titled "Casper Customer Behavior Forecast Application" embodies an innovative and academic effort aimed at enhancing customer engagement and predicting customer behavior within the context of business-to-business (B2B) interactions. This research project carries considerable importance, both in its potential to reinvent the field of customer relationship management and its ability to contribute to the global academic conversation on machine learning and deep learning.

Ground Improvement and Monitoring for a Reclamation on Reclaimed Soft Soil in Singapore

Land reclamation works for land development and harbor construction generally require large amounts of sand. At the same time the dredging of access channels, berth pockets and turning circles for shipping may generate both, ‘suitable’ and ‘unsuitable’ dredged sediments. The definition of suitable in general focusses on well compactable granular material with a low fines content. However, in the present times of environmental awareness and search for sustainable solutions, the offshore dumping and disposing of ‘unsuitable’ fine-grained material is not deemed sustainable anymore. In this paper the Tuas Terminal Phase 1 (TTP1) in Singapore will be discussed. All dredged soft clay and residual soils were used in the reclamation and capped with a layer of limited thickness of clean sand. To achieve the design requirements, ground improvement by means of surcharge with prefabricated vertical drains (PVD’s) was performed. To demonstrate the effectiveness and results of the ground improvement works, extensive monitoring by means of settlement beacons and clusters of extensometers and porewater pressure meters was setup. Even on-site developed ‘floating’ piezometers were developed to monitor the consolidating slurry. The execution of these works required adapted execution and advanced design methods. The design and prediction of the behavior of the sediments during improvement and on the long term was a challenge. The correct prediction of deformations and the level of the soft soil-granular soil interface was important to guarantee the correct sand cap thickness after ground improvement. The long-term behavior after overconsolidation through ground improvement was studied in detail. This has led to the development of a more correct prediction method to estimate the secondary settlements of PVD-improved overconsolidated soft soil. The reclamation of the TTP1 terminal proved to be a success where stringent design requirements were met while re-using ‘unsuitable’ material and minimizing the import of clean sand.

Integrating pollutant levels and biochemical biomarkers in oysters (Crassostrea rhizophorae and Crassostrea gasar) indicates anthropic impacts on marine environments along the coast of Santa Catarina state, Brazil

This study aimed to carry out a general diagnosis of the contamination of the coastal marine environment of the Santa Catarina state (SC, Brazil) by different classes of environmental pollutants, as well as to evaluate possible adverse effects of the contaminants on biochemical biomarkers of oysters, Crassostrea gasar and Crassostrea rhizophorae. 107 chemicals were evaluated in water, sediment and oyster samples from nine sites along the coastline of SC. We also examined various biochemical biomarkers in the oysters' gills and digestive glands to assess potential effects of contaminants. In general, the northern and central regions of the littoral of SC presented higher occurrences and magnitudes of contaminants than the southern region, which is probably related to higher urbanization of center and northern areas of the littoral. The biomarker analysis in the oysters reflected these contamination patterns, with more significant alterations observed in regions with higher levels of pollutants. Our results may serve as a first baseline for future and more extensive monitoring actions and follow-up of the degree of contamination in the state, allowing for inspection actions and management of areas most affected by marine pollutants.

Mountain soil multitrophic networks shaped by the interplay between habitat and pedoclimatic conditions

Our knowledge of the factors influencing the distribution of soil organisms is limited to specific taxonomic groups. Consequently, our understanding of the drivers shaping the entire soil multitrophic network is constrained. To address this gap, we conducted an extensive soil biodiversity monitoring program in the French Alps, using environmental DNA to obtain multi-taxon data from 418 soil samples. The spatial structure of resulting soil multitrophic networks varied significantly between and within habitats. From forests to grasslands, we observed a shift in the abundance of trophic groups from fungal to bacterial feeding channels, reflecting different ecosystem functioning. Furthermore, forest soil networks were more strongly spatially structured which could only partly be explained by abiotic conditions. Grassland soil networks were more strongly driven by plant community composition and soil characteristics. Our findings provide valuable insights into how climate and land-use changes may differentially affect soil multitrophic networks in mountains.

Water for human consumption in Zagreb city as a possible source of some contaminants

In the Zagreb city area extensive monitoring of water for human consumption has been carried out for years, which includes several dozen different chemical, biological and physical parameters. Given the daily water intake in our bodies, the possibility of ingesting certain contaminants via water has a significant role. This especially applies to nitrates, which are in the second place of importance, since it has been found that the water is their source. In this paper, we present results of the presence for the following contaminants: aluminum, cadmium, chlorate and chlorite, nitrate and mercury in water that can be consumed via water in the city of Zagreb for the period 2016-2020. The results indicate stable average annual values of monitored contaminants, except for aluminum and chlorates, for which it was found that the values of annual averages differ several times. However, even in cases of higher values, their contribution to water intake does not pose a risk to the health of consumers, nor it significantly contribute to their overall intake. Still, given the assumption that this is a specific exposure, to which the consumer is tied to the place of residence, it is important to constantly monitor trends, so that each exposure is kept to a minimum.

Active and Intelligent Biodegradable Packaging Based on Anthocyanins for Preserving and Monitoring Protein-Rich Foods.

Currently, active and intelligent packaging has been developed to solve the spoilage problem for protein-rich foods during storage, especially by adding anthocyanin extracts. In such a film system, the antioxidant and antibacterial properties were dramatically increased by adding anthocyanins. The physicochemical properties were enhanced through interactions between the active groups in the anthocyanins and reactive groups in the polymer chains. Additionally, the active and intelligent film could monitor the spoilage of protein-rich foods in response to pH changes. Therefore, this film could monitor the sensory acceptance and extend the shelf life of protein-rich foods simultaneously. In this paper, the structural and functional properties of anthocyanins, composite actions of anthocyanin extracts and biomass materials, and reinforced properties of the active and intelligent film were discussed. Additionally, the applications of this film in quality maintenance, shelf-life extension, and quality monitoring for fresh meat, aquatic products, and milk were summarized. This film, which achieves high stability and the continuous release of anthocyanins on demand, may become an underlying trend in packaging applications for protein-rich foods.

Improving integrated depression and non-communicable disease care in Malawi through engaged leadership and supportive implementation climate

BackgroundLow- and middle-income countries often lack access to mental health services, leading to calls for integration within other primary care systems. In sub-Saharan Africa, integration of depression treatment in non-communicable disease (NCD) settings is feasible, acceptable, and effective. However, leadership and implementation climate challenges often hinder effective integration and quality of services. The aim of this study was to identify discrete leadership strategies that facilitate overcoming barriers to the integration of depression care in NCD clinics in Malawi and to understand how clinic leadership shapes the implementation climate.MethodsWe conducted 39 in-depth interviews with the District Medical Officer, the NCD coordinator, one NCD provider, and the research assistant from each of the ten Malawian NCD clinics (note one District Medical Officer served two clinics). Based on semi-structured interview guides, participants were asked their perspectives on the impact of leadership and implementation climate on overcoming barriers to integrating depression care into existing NCD services. Thematic analysis used both inductive and deductive approaches to identify emerging themes and compare among participant type.ResultsThe results revealed how engaged leadership can fuel a positive implementation climate where clinics had heightened capacity to overcome implementation barriers. Effective leaders were approachable and engaged in daily operations of the clinic and problem-solving. They held direct involvement with and mentorship during the intervention, providing assistance in patient screening and consultation with treatment plans. Different levels of leadership utilized their respective standings and power dynamics to influence provider attitudes and perceptions surrounding the intervention. Leaders acted by informing providers about the intervention source and educating them on the importance of mental healthcare, as it was often undervalued. Lastly, they prioritized teamwork and collective ownership for the intervention, increasing provider responsibility.ConclusionTraining that prioritizes leadership visibility and open communication will facilitate ongoing Malawi Ministry of Health efforts to scale up evidence-based depression treatment within NCD clinics. This proves useful where extensive and external monitoring may be limited. Ultimately, these results can inform successful strategies to close implementation gaps to achieve integration of mental health services in low-resource settings through improved leadership and implementation climate.Trial registrationThese findings are reported from ClinicalTrials.gov, NCT03711786. Registered on 18/10/2018. https://clinicaltrials.gov/ct2/show/NCT03711786.

Scales of Connectivity within Stream Temperature Networks of the Clackamas River Basin, Oregon

Water quality varies along the stream network; thus, considering the directional, dendritic nature of stream networks with surrounding landscape variables is essential in explaining spatial variations of water quality. Using a spatially extensive stream temperature monitoring effort in the Clackamas River Basin in the United States, we first compare spatial scales of analysis of atmospheric, landscape, and in-stream explanatory variables through their correlation with summer stream temperatures. We then derive a predictive stream temperature model with factors representing the spatial variation of local climate, recent wildfire effects, and discharge. Finally, we compare nonspatial multiple linear regression to a spatial stream network (SSN) model to assess the combined importance of the spatial scale of analysis and flow-connected stream distance in explaining total variation in stream temperatures. Most explanatory variables show the most highly significant relationships to stream temperature when derived as a percentage of the total upstream area above observation sites. Elevation and vegetation cover, however, were most significantly correlated to stream temperature at the riparian buffer area scale and the local reach contributing area scale, respectively. Multiple regression analysis using total upstream burned area, total upstream area with underlying High Cascades geology, and the elevation within the 100-m-wide riparian area explained 81 percent of variation in stream temperature. SSN outperformed this nonspatial statistical model, however, in explaining the total variation in stream temperature. These comparisons of scaled data sets demonstrate both the local and cumulative upstream effects on stream temperature, providing a spatial network-informed framework to those prioritizing watershed restoration and wildfire recovery activities.

Leveraging monitoring, evaluation, and learning to scale the Enabling Inclusion® program for children with disabilities in India and globally.

Children with disabilities in low- and middle-income countries face many challenges and lack adequate services, including access to rehabilitation professionals. To address this lack of access, Amar Seva Sangam Ayikudy (ASSA), a non-governmental organization (NGO) in India, designed a technology-leveraged rehabilitation program called Enabling Inclusion® (EI®), and implemented it in one state (Tamil Nadu, India) before scaling it. The model is supported by the EI® app, which enables organizations to screen, assess and monitor progress of children with disabilities via rehabilitation specialists and community rehabilitation workers, and to provide family-centered, goal-based interventions. An extensive monitoring, evaluation, and learning (MEL) framework is embedded into the program. This paper explores how this MEL system supported the scaling of the EI® model, reaching additional beneficiaries nationally and globally. This paper describes ASSA's MEL framework and demonstrates its use for decision-making in the process of scaling. It also explores how collaborations with various government departments, NGOs, and private partners contributed to the scaling of the EI® model and technology. Scaling of the EI® program was achieved by (1) expansion of the program in rural Tamil Nadu (vertical scale-up) in partnership with the Tamil Nadu government and private partners, and (2) by licensing the EI® app and model to other NGOs in various states in India and globally (horizontal scale-up). Systematic examination of key program and performance indicators, as well as stakeholder feedback, informed decisions to modify the EI® app over time. This included further customizing to the needs of children and service providers, covering a greater range of age groups and contexts, and modifying service delivery models. Child functional independence, participation, and inclusion was further strengthened by mobilizing parent empowerment groups, community awareness programs, school advocacy, and entitlements from the government. Flexibility in the implementation model of the EI® app allowed for adaptation to local contexts and organizations, and facilitated its scale-up. A dynamic, inclusive, and locally grounded MEL system, a flexible and collaborative approach, and an adaptive implementation model increased the accessibility of an early intervention and childhood rehabilitation program for children with disabilities and their families throughout the state of Tamil Nadu, across India, and internationally.

Changes in sea floor productivity are crucial to understanding the impact of climate change in temperate coastal ecosystems according to a new size-based model

The multifaceted effects of climate change on physical and biogeochemical processes are rapidly altering marine ecosystems but often are considered in isolation, leaving our understanding of interactions between these drivers of ecosystem change relatively poor. This is particularly true for shallow coastal ecosystems, which are fuelled by a combination of distinct pelagic and benthic energy pathways that may respond to climate change in fundamentally distinct ways. The fish production supported by these systems is likely to be impacted by climate change differently to those of offshore and shelf ecosystems, which have relatively simpler food webs and mostly lack benthic primary production sources. We developed a novel, multispecies size spectrum model for shallow coastal reefs, specifically designed to simulate potential interactive outcomes of changing benthic and pelagic energy inputs and temperatures and calculate the relative importance of these variables for the fish community. Our model, calibrated using field data from an extensive temperate reef monitoring program, predicts that changes in resource levels will have much stronger impacts on fish biomass and yields than changes driven by physiological responses to temperature. Under increased plankton abundance, species in all fish trophic groups were predicted to increase in biomass, average size, and yields. By contrast, changes in benthic resources produced variable responses across fish trophic groups. Increased benthic resources led to increasing benthivorous and piscivorous fish biomasses, yields, and mean body sizes, but biomass decreases among herbivore and planktivore species. When resource changes were combined with warming seas, physiological responses generally decreased species’ biomass and yields. Our results suggest that understanding changes in benthic production and its implications for coastal fisheries should be a priority research area. Our modified size spectrum model provides a framework for further study of benthic and pelagic energy pathways that can be easily adapted to other ecosystems.

Pregnant women with diabetes and their clinician's experience of participating in a pilot randomised controlled trial of corticosteroid administration in late pregnancy: A qualitative study.

Little research exists to support the administration of corticosteroids to pregnant women with diabetes. Pregnant women are often excluded from clinical trials due to concerns of harm to the foetus. This study aimed to understand the experiences of women and clinicians of participating in the Prevention of neonatal Respiratory distress with antenatal corticosteroids before Elective Caesarean section in women with Diabetes pilot randomised controlled trial to determine the acceptability of the study protocol. Women and clinicians participating in the pilot trial were invited to complete a telephone interview regarding their experiences of participating. Qualitative data were collected and subsequently analysed using thematic analysis. A total of 13 women and nine clinicians were recruited between June 2020 and May 2022 for a telephone interview. Participating in the study was deemed acceptable by women and clinicians. Women chose to participate in the study due to the perceived low risk of harm associated with the intervention and for altruistic reasons. The high level of clinical support and information provided for the duration of the pilot trial was valued by women and clinicians. All clinicians highlighted the importance of conducting the trial to inform evidence-based practice. Pregnant women are more likely to participate in clinical trials when perceived risks are low and they are well-informed during decision-making. Clinicians will support clinical trials when they perceive a benefit to practice and feel assured that women receive extensive monitoring and support. Incorporating these factors into study protocols is more likely to be successful in recruiting pregnant women and maintaining the engagement of clinical staff for the duration of clinical trials. Patients were invited to be participants in this study. A consumer has been included in the planning and oversite of the large multicentre trial.

Community-based plant diversity monitoring of a dense-canopy and species-rich tropical forest using airborne LiDAR data

Tropical forests are widely regarded as the Earth’s most important ecosystems, yet they are severely threatened by anthropogenic disturbances. Rapid and extensive monitoring of forest structure and biodiversity is crucial for developing ecologically sound conservation and restoration strategies. Airborne light detection and ranging (LiDAR) can effectively monitor three-dimensional forest canopy structures. Traditionally, LiDAR-based plant diversity estimation has relied on individual tree-based and area-based approaches. However, these approaches face significant challenges in tropical forests due to their complex canopy structures and diverse plant compositions. Therefore, by proposing a novel community-based approach, this study aims to examine the relationship between field-derived biodiversity indices and LiDAR-derived canopy structural metrics of plant communities in a species-rich ForestGEO forest dynamic plot in tropical Hong Kong. Our goal is to determine whether canopy structural metrics extracted from airborne LiDAR data can serve as a robust and efficient alternative for expediting plant diversity monitoring in highly dense and diverse tropical forests. Our results indicate that an integration watershed segmentation technique (for automatic patch-scale plant community delineation), LiDAR-derived canopy structural metrics, and machine learning-based random forest regression analysis can provide accurate predictions of community-based species diversity indices. Among various diversity indices, species richness and the Shannon-Wiener index are most accurately estimated using LiDAR-derived metrics. This study reveals that species richness is predominantly influenced by the existence of multi-layered canopy structures, whereas the Shannon-Wiener index is associated with both multi-layered structures and canopy morphologies. Overall, our findings showcase the immense potential of airborne LiDAR data in advancing the monitoring of structure and biodiversity in dense-canopy and species-rich tropical forests in a spatially explicit manner.

Grouting effects in rectangular pipe jacking: centrifuge modelling and field data

Extensive field monitoring and a series of centrifuge model tests of grouting for rectangular pipe jacking were carried out to investigate the mechanism of grouting and its effects. An innovative jacking grouting device was developed to model the entire grouting process in a centrifuge test. Validation work was carried out by comparing the ground settlement between the centrifuge model test results and the field results, and reasonable agreement was obtained. The experimental results show that grouting slurry with low fluid loss and high viscosity is more conducive to controlling ground settlement during tunnelling; the ground settlement is strongly influenced by the grouting pressure. Within a certain range, increasing grouting pressure is conducive to controlling settlement; however, excessive grouting pressure will lift the ground surface.

A STUDY ON STATE WISE AIR POLLUTION WITH REGARD TO PARTICULATE MATTER IN INDIA

This study looks at air quality in various Indian states, focusing on the prevalence and composition of particulate matter (PM) as a key indicator of air pollution. The study takes a comprehensive approach to assessing state-by-state variations in PM concentrations, taking into account a variety of geographical, climatic, and demographic factors. The research aims to identify patterns, trends, and potential sources of PM in different regions by utilizing extensive air quality monitoring data, statistical analyses, and geographical information systems (GIS).The environment and public health are seriously threatened by air pollution, particularly when it comes to particulate matter (PM). PM is composed of minuscule particles suspended in the atmosphere, which are often generated by combustion, natural, and industrial processes. Prolonged exposure to high particulate matter has been linked to respiratory and cardiovascular disorders, as well as detrimental effects on the environment. Using data from the Air Quality Life Index (AQLI) for five years, from 2016 to 2021, and breaking down each state separately, this study examines the intricate rise of PM in India in great detail. Uttar Pradesh has the highest PM rate (2021) at 5.754. This implies that there has been a discernible increase in air pollution in this state. With the lowest is Arunachal Pradesh

Long-term hydrological monitoring of soils in the terraced environment of Cinque Terre (north-western Italy)

Terraced landscapes represent one of the most widespread human-induced/man-made transformations of hilly-mountainous environments. Slope terracing produces peculiar morphologies along with unusual soil textures and stratigraphic features, which in turn strongly influence slope hydrology. The investigation of the hydrological features of terraced soils is of fundamental importance for understanding the hydrological dynamics occurring in these anthropogenic landscapes, especially during rainfall events. To this purpose, the availability of extensive field monitoring data series and of information on subsoil properties and structure is essential. In this study, multi-sensor hydrological data were acquired over a period longer than 2 years in the experimental site of Monterosso al Mare, in the Cinque Terre National Park (Liguria region, Italy), one of the most famous examples of terraced landscape worldwide. Monitoring data were coupled with accurate engineering-geological investigations to achieve the hydro-mechanical characterization of backfill soils and to investigate their hydrological response at both the seasonal and the single rainstorm scale. The results indicated that the coarse-grained, and anthropically remolded texture of the soils favors the rapid infiltration of rainwater, producing sharp changes in both soil volumetric water content and pore water pressure. Furthermore, the pattern of hydrological parameters showed seasonal trends outlined by alternating phases of slow drying and fast wetting. The study outcomes provide useful insights on the short and long-term evolution of hydrological factors operating in agricultural terraces. These findings represent a useful basis for a better understanding of the time-dependent processes that guide water circulation in terraced systems, which have a key role in controlling the occurrence of erosion and landslide processes.

Monintainer: An orchestration-independent extensible container-based monitoring solution for large clusters

Container virtualization has recently gained popularity due to its low performance and resource allocation overhead. The rise of this technology can be attributed to the advancement of cloud computing and the adoption of micro-services architecture. These new approaches offer a more efficient and fine-grained system design through the benefits of containerization, such as isolation, portability, and improved performance. However, container-based systems have created new challenges in monitoring due to their automated flexibility, ephemerality, and the increasing number of containers in a system. So there is a practical need for effective monitoring and performance management tools. This paper analyses the key performance metrics for machine, container and application services, including CPU usage, memory usage, disk usage, and network usage. Furthermore, we review several widespread tools for collecting and monitoring these metrics and present the Monintainer tool. It is a solution designed to monitor entire container-based systems, from applications to their underlying infrastructure, allowing users to better understand their systems’ behavior in run-time. The tool’s results can aid container-based systems’ design, implementation and optimization.

Sediment deposition within cascade reservoirs: a case study of Baihetan Reservoir in the lower Jinshajiang River, China

Sediment deposition in cascade reservoirs is not only related to the utilization efficiency of the reservoir itself but also to the boundary conditions for the operation of other reservoirs in the same group. The Baihetan Reservoir is the largest hydropower project with the highest unit capacity in the world, and it is necessary to consider sediment deposition within it, as this affects the comprehensive operation of cascade reservoirs in the lower Jinshajiang River. In this study, the input water, sediment, and deposition characteristics were analyzed based on both field hydrological and topographic data of the Baihetan Reservoir during its initial impoundment period. The results showed that water entering Baihetan Reservoir was mainly derived from the upper main stream, and approximately 41% was concentrated in the third quarter. Ten times the amount of sediment derived from the main stream was received from tributaries and uncontrolled areas of the reservoir, and these are the main sediment input sources. The fluctuating backwater area influenced by the upstream Wudongde Reservoir was slightly eroded, and siltation mainly occurred in the dead storage capacity (below 765 m) of the main stream and tributary estuaries in the perennial backwater area; approximately 15.8 times that in the regulating storage capacity (between 765 and 785 m). The differences between the results of this study and those from the reservoir demonstration stage indicate that was a lack of understanding about how climate change, human activities, and uncontrolled areas would affect siltation patterns. In future projects, research focusing on climate trend analyses and the comprehensive consideration of human activities should be combined with extensive sediment production monitoring and model parameter calibration.

Spatial–Temporal Mapping and Landscape Influence of Aquaculture Ponds in the Yangtze River Economic Belt from 1985 to 2020

Most current research on aquaculture ponds focuses on coastal areas, leaving a gap in understanding of inland regions, such as the strategically significant Yangtze River Economic Belt in China. This study introduces an intelligent extraction method for extensive monitoring of aquaculture ponds in Yangtze River Economic Belt, using Landsat and Sentinel data from 1985 to 2020 with five-year intervals based on the Google Earth Engine (GEE) platform. Land cover change data were also analyzed to understand the impact of aquaculture-related changes. Results indicate a significant increase in aquaculture ponds in the Yangtze River Economic Belt from 3235.51 km2 to 14,207.08 km2 between 1985 and 2020. Aquaculture activity primarily shifted eastward from 1985 to 2015, then westward from 2015 to 2020. Approximately 2018.36 km2 of aquatic areas underwent conversion, mainly to water bodies or croplands, with fewer transitions to impervious surfaces, grasslands, or forests. This study highlights that inland areas can also experience significant increases in aquaculture ponds, particularly alongside large rivers, and that the environmental impacts of these changes differ from those in coastal areas, warranting specific attention.

AI-produced certainties in health care: current and future challenges

AbstractSince uncertainty is a major challenge in medicine and bears the risk of causing incorrect diagnoses and harmful treatment, there are many efforts to tackle it. For some time, AI technologies have been increasingly implemented in medicine and used to reduce medical uncertainties. What initially seems desirable, however, poses challenges. We use a multimethod approach that combines philosophical inquiry, conceptual analysis, and ethical considerations to identify key challenges that arise when AI is used for medical certainty purposes. We identify several challenges. Where AI is used to reduce medical uncertainties, it is likely to result in (a) patients being stripped down to their measurable data points, and being made disambiguous. Additionally, the widespread use of AI technologies in health care bears the risk of (b) human physicians being pushed out of the medical decision-making process, and patient participation being more and more limited. Further, the successful use of AI requires extensive and invasive monitoring of patients, which raises (c) questions about surveillance as well as privacy and security issues. We outline these several challenges and show that they are immediate consequences of AI-driven security efforts. If not addressed, they could entail unfavorable consequences. We contend that diminishing medical uncertainties through AI involves a tradeoff. The advantages, including enhanced precision, personalization, and overall improvement in medicine, are accompanied by several novel challenges. This paper addresses them and gives suggestions about how to use AI for certainty purposes without causing harm to patients.