Collaborative Monitoring Research Articles

Empowering Indigenous-Led Contaminant Monitoring through Collaborative Partnerships and Two-Way Capacity Sharing

Climate-driven landscape change, legacy waste and ongoing infrastructural investment are leading to concerns about contaminant release in Arctic Indigenous and Local communities. Sustainable development that considers threats posed by accelerating environmental change requires accessible, appropriate, and sustained environmental data to inform strategic decision-making. Collaborative partnerships and capacity sharing are necessary to promote resilient and sustainable environmental monitoring approaches, however effective collaboration has been hindered by mismatches in priorities and timelines between communities and research programs. We outline the development and later evolution of a community-based environmental research program focused on monitoring contamination threats posed by legacy infrastructure sourced from industry, transportation and domestic waste sites. Capacities and insights from diverse knowledge systems guided each stage of our research approach. Reflections provided by an Indigenous and non-Indigenous Early Career Researcher (ECR) share insights into different aspects of the research process. We highlight how cross-cultural partnerships and capacity sharing have enabled evolving and reflexive community-based contaminant monitoring. Our approach facilitates a structural shift from collaborative monitoring with external analysis to autonomous monitoring that supports equity in research outcomes. Appropriately considered and resourced co-development at regular points of the research process has been critical to developing a complete and effective Indigenous-led research project.

Project Monitoring Based on View of All Project Partners At Digital Project Management Platforms

In the dynamic landscape of digital project management, effective project monitoring is critical for successful project outcomes. The emergence of digital project management platforms has revolutionized the way projects are monitored, enabling real-time collaboration and data-driven decision-making. Periodic site visits and gathering input from all project stakeholders are crucial for ensuring clear understanding of project progress by states institutional. With digital project management systems, on-site progress should be monitored in real-time and inclusive of all project stakeholders. The research investigates how collaborative monitoring fosters project progress of transparency and active engagement among project partners. By providing real-time visibility into project progress, tasks, and milestones, these platforms empower stakeholders with up-to-date information, enabling agile decision-making and prompt risk mitigation. This study explores how digital project management platforms integrate site monitoring evaluations and reporting tools, providing valuable data-driven insights into project performance. By considering periodic analytics, project partners can improve resource allocation, decision making process against possible bottlenecks at construction stages and boost productivity and inform states institutional to monitor project progress closely. Furthermore, the research examines the influence of these platforms on communication dynamics, promoting communication and efficient information exchange among project partners. Such enhanced communication capabilities result in improved team cohesion and collaboration, driving the project forward with synchronized efforts. In this study, the focus lies on tracking the progress of contractual agreements through digital platforms, enabling all stakeholders to monitor advancements. Specifically, the investigation centers on how the designated contractor responsible for project execution and the consulting firm appointed by the project owner can effectively track project progress on a periodic basis using digital platforms. In the context of transportation projects in Turkey, a digital project tracking system has been developed for the Ministry of Transport and Infrastructure, through which data obtained from on-site visits and consultant opinions regarding project advancements are shared with the Ministry. Subsequently, the responsible contracting authority, guided by the consultant's feedback, can respond and evaluate progress accordingly. This approach ensures that project advancements are accurately monitored by all stakeholders of the project. To do so, this study investigates the utilization of the prepared digital platform to facilitate rapid and precise contract monitoring by all project stakeholders, encompassing the opening information and assessment evaluations based on stakeholders' perspectives.

Dispersal of a wolf with complex ancestry from the Dinaric–Alpine region to Germany demonstrates the value of transboundary monitoring networks

AbstractLarge carnivores such as wolves (Canis lupus) at times disperse distances of several hundred kilometers, which may enable gene flow over vast distances. Such long‐distance dispersal events, however, often remain undetected, and documenting long‐distance dispersers and their movements typically depend on strong transboundary collaborations. Here, we report the dispersal of a wolf (MSV0FF) from the Alpine region of northern Slovenia to Bavaria in southern Germany, a straight‐line distance of around 300 km. The disperser originated in a pack with a genetically diverse breeding pair, where the father exhibited ancestry from the Dinaric and Italian Alpine populations. Genetic analysis of the mother indicated that she was an immigrant from a divergent population further south in the Dinaric‐Balkan region. Such a varied ancestry augments the probability of immigrants being genetically misclassified as wolf‐dog hybrids in their area of arrival, which increases the risk of unfavorable management decisions toward individuals that would be genetically highly valuable for the recipient population. Wolf MSV0FF therefore demonstrates the benefits of international collaborative monitoring networks and the value of sharing samples and analytical approaches for the monitoring of wide‐ranging species.

A novel method of reliable data transmission for Internet of Vehicles based on crowd sensing strategy

SummaryThe Internet of Vehicles (IoV) is a hot research topic in intelligent transportation, and the research on new methods of intelligent data transmission is one of the key contents. Aiming at the application scenario of urban air quality monitoring data collection and transmission based on IoV, a new reliable data transmission method (Vehicular Grouping‐Communicated Data [VGCD]) based on crowd sensing strategy is proposed. This method is designed to utilize the intelligent perception and collaborative monitoring of vehicle to collect data, avoiding redundancy and overload of information; a reliable data transmission minimum delay hybrid routing method is proposed in the data transmission part, which combines encoding mechanism with routing design, integrates routing switching ideas, predicts vehicle adaptive connectivity based on fuzzy logic, and makes probability based routing decisions to minimize delay, achieving reliable and efficient data collection and transmission. The proposed new method has important theoretical significance and practical value for various applications such as dynamic remote perception monitoring based on the IoV in intelligent transportation.

Collaborative monitoring of wind turbine performance based on probabilistic power curve comparison

A wind farm is usually equipped with multiple wind turbines of the same type. These wind turbines often work under same complex conditions. Accurate performance degradation monitoring is crucial for ensuring the reliable operation of wind farms and reducing maintenance costs. Motivated by this, this article develops a new wind turbine performance degradation monitoring scheme, which is based on pairwise comparison of the probability power curves of different wind turbines in a wind farm. Firstly, covariate matching is used to eliminate the inherent differences in meteorological variables of different turbines within the same data segment. Next, two probabilistic wind power curves, the quantile power curve and density power curve, model the functional relationship between the meteorological variables and wind power output. Then, deviation vectors are generated by calculating the deviation of probabilistic power curves between each pair of wind turbines. Finally, a directional Hotelling T2 control chart is proposed to monitor the deviation vectors. We apply the new method on the real data of a wind farm in East Britain. Results show that the proposed monitoring technique can monitor wind turbine performance degradation more precisely and comprehensively than the existing approaches.

Building collaborative monitoring programs to manage for ecological sustainability in multi-use marine protected areas: Parks Canada’s national marine conservation areas

Parks Canada collaborates with partners to manage a growing system of national marine conservation areas (NMCAs) and national marine conservation area reserves (NMCARs). These ecologically and culturally varied marine protected areas are established to represent the rich diversity of Canada’s oceans and Great Lakes. They support a broad range of uses within specific zones, including recreation, tourism, fishing, shipping, and Indigenous rights-based activities. Long-term monitoring is a critical tool for tracking changes in marine ecosystems, developing strategies to protect and conserve ecological and cultural values, responding to environmental threats, and ensuring ecologically sustainable use of marine resources that provide long-term societal benefits. Monitoring involves working closely with Indigenous partners, federal departments, provincial/territorial governments, coastal communities, and others (e.g., fishing and tourism sectors, non-governmental organizations, academia). As part of larger seascapes, NMCAs are managed using an ecosystem approach, which requires information on the state of marine biodiversity, threats, and pressures on marine ecosystems, including levels or impacts of marine uses. Each NMCA monitors a site-specific suite of indicators covering these key characteristics that are then integrated to inform decision-making. The monitoring framework is intended to enable a comprehensive, nationally consistent approach to monitoring and reporting while adapting to local needs and governance. Successes, challenges, and lessons learned through the implementation of the framework are highlighted through place-based examples.

Earthworm records and habitat associations in the British Isles

The National Earthworm Recording Scheme (NERS) is the most comprehensive national database of earthworm species occurrence records for the British Isles, and possibly for any individual country in the world. Utilising the NERS database, we sought to update the current knowledge of earthworm species occurrences in the UK, Ireland and Channel Islands; identify species-specific habitat and microhabitat associations; reveal any biases and complementarities between amateur naturalist and research-related earthworm record collection; and inform how future earthworm sampling can be better focussed to improve our knowledge of earthworm ecology. We found that the most commonly occurring earthworm species were present in farmland and woodland, and recovered via soil pit sampling, the most common habitat-sampling protocol combinations. However, several earthworm species showed specificity to alternative habitats (such as trees, wetlands, and compost), and association with microhabitat (non-soil) sampling. There were clear disparities between scientific researchers and amateur naturalist recorders in terms of habitat types visited and sampling protocols/microhabitats used in the collection of earthworm records. Most importantly, we found that earthworm species currently considered to be nationally ‘rare’ in the British Isles are significantly associated with the most under-represented habitat-protocol/microhabitat combinations (forest deadwood and other microhabitats, in addition to scrubland, wetland and heathland habitats), and thus may not be rare, only under-sampled. We therefore encourage earthworm researchers and recorders to give greater attention to these situations, to gain new insights into these earthworm species' ecologies and distributions. Finally, we would like to promote the establishment of earthworm recording schemes in other countries, to enable national and global collaborative monitoring of earthworm responses to environmental change.

Implantable sensing technology for civil engineering structures

Providing crack location and stiffness variation information using practical structural health monitoring technology is highly significant for in-service structures. Inspired by the fast development of implantable devices, this paper introduced an implantable sensing technology (IST) for health monitoring of a typical concrete member known as shear wall undergoing loading progression. Two collaborative monitoring steps were developed to acquire targeted damage information at different loading stages, the one was, a cylindrical concrete implantable module (CCIM) sensor-enabled IST was applied for monitoring and imaging the early cracks of the shear wall at the initial loading stage; and the other was, a concrete implantable bar (CIB) sensor-enabled IST was employed for monitoring and assessing the damage evolution at the mid-late loading stage. Experimental results showed that these two collaborative ISTs can accurately identify the early cracking regions of the shear wall and reveal its stiffness degradation processes throughout the loading.

Rigorous assessment of a unique tiger recovery in Southeast Asia based on photographic capture-recapture modeling of population dynamics

Tigers and other large predatory carnivores have suffered population extirpations and range contractions. This is particularly true for tiger populations in southeastern Asia, which harbours one-third of their remaining habitats. In stark contrast, a sustained recovery of a wild tiger population has occurred between 2007 and 2023, in three reserves of Thailand: Huai Kha Khaeng (HKK), Thung Yai East (TYE) and Thung Yai West (TYW), which together cover 6470 km2 (36%) of the larger Western Forest Complex (WEFCOM). We quantitatively monitored this recovery employing closed and open model analyses of data from photographic capture-recapture sampling. The resulting estimates of tiger population dynamic parameters showed: mean (±SE) tiger abundance annually varied from 36 (1.0) to 79 (1.53) in HKK, 2 (0.26) to 20 (4.45) in TYE and 3 (0.26) to 44 (2.11) in TYW, driven by mean annual survival rates of 0.79 (0.02) in HKK, 0.72 (0.05) in TYE, and 0.69 (0.05) in TYW. The annual numbers of recruits fluctuated from 0 (1.69) to 33 (1.93) tigers in HKK, 0 (0.47) to 13 (0.57) in TYE and 0 (1.13) to 36 (2.28) in TYW. Overall, the mean tiger population densities/100 km2 ranged between 1.3 (0.19) and 2.9 (0.29) in HKK, 0.2 (0.08) and 1.8 (0.34) in TYE, and 0.2 (0.07) and 3.1 (0.56) in TYW. Generally, the tiger population trended upward, with reserves protected over longer periods leading the tiger recovery. Our results are further backed by ancillary records on births of 67 cubs, 47 tiger dispersal events, as well as the recovery corresponding with incremental spatio-temporal coverage by the patrols. Cumulatively, our results provide evidence that effective law enforcement should be a critical component for achieving tiger population recoveries in Asia. Alternative conservation strategies that ignore this component do not appear to be evidence-based. Our results also demonstrate the utility of the independent collaborative monitoring framework adopted by the Thailand Government.

Enhancing stability analysis of open-pit slopes via integrated 3D numerical modeling and data monitoring

The slope at Zhahanur open-pit mine (China) has experienced significant and prolonged deformation, posing a considerable risk to life and property within the mining area. To analyse the deformation and failure mechanism of the rock slope, geotechnical investigations, numerical simulations, and long-term movement monitoring were undertaken. In order to improve the efficiency of slope pretreatment, a point elimination method was proposed for the slope surface, and a 3D geological model of mining area was established. A systematic analysis was performed on monitoring data from surface slope radar and underground inclinometers, enabling the identification of the potential sliding surface and deformation pattern of northern slope in a rational manner. Additionally, through three-dimensional numerical simulation, the feasibility of the mining scheme that incorporates waste dump inside the pit was explored. These simulations accurately captured the varying deformation patterns at different levels and exhibited good agreement with available field monitoring data. The findings revealed a composite multi-stage slope failure pattern primarily controlled by weak layers. The safety factors associated to two competing slip surfaces were determined to be 1.20 and 1.15, respectively. To ensure the safe extraction of coal resources from the deformed slope, a combined mining scheme of in-pit dumping and mining is proposed in the study. The proposed scheme was shown to meet the production needs, with a minimum safety factor of 1.08 for rock slope under the mining scheme. By integrating various field monitoring techniques with numerical simulations, a collaborative monitoring scheme was devised to effectively and promptly identify potential sliding surface and implement necessary mining and control measures to ensure slope stability. This comprehensive approach provides valuable insights into the deformation behavior of the rock slope and facilitates the implementation of proactive measures to mitigate risks associated with slope instability.

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process. The study delves into the spatiotemporal evolution patterns of infrared radiation and acoustic emission in rock under loading. Utilizing stress, cumulative acoustic emission count, and average infrared radiation temperature (AIRT), the paper establishes a comprehensive evaluation model termed “acoustic-thermal-stress” fusion information, employing principal component analysis (PCA). The research reveals that the sensitivity to rock sample damage response follows the sequence of cumulative acoustic emission count, AIRT, and stress. Furthermore, a novel method for identifying rock fracture precursors is proposed, based on the first derivative of the comprehensive evaluation model. This method addresses the limitations of single physical field information, enhancing the robustness of monitoring data. It determines the average stress level of fracture precursors to be 0.77σmax. Subsequently, the study defines the probability function of rock damage during loading and fracture, enabling the realization of probability-based warnings for rock fracture. This approach introduces a new perspective on rock fracture prediction, significantly contributing to safety monitoring and warning systems in mine safety and geotechnical engineering. The findings of this research hold paramount engineering significance, offering valuable insights for enhancing safety measures in such projects.

Layer-by-layer self-assembly of durable, breathable and enhanced performance thermoelectric fabrics for collaborative monitoring of human signal

Thermoelectric devices have received widespread attention in next-generation flexible electronics due to their excellent self-powered characteristics, multifunctionality and high integrability. However, current thermoelectric devices suffer from drawbacks such as poor durability, complex preparation processes, and terrible air permeability which is the necessary metric in wearing comfort. Here, we propose a layer-by-layer (LBL) self-assembly strategy to prepare multifunctional thermoelectric fabrics with enhanced thermoelectric performance and durability. Based on the self-assembly effect, the interfacial bonding between the thermoelectric material and the fabric substrate is effectively improved, so that the thermoelectric properties of the fabrics can still be maintained stable after 2000 bending cycles and 600 washing cycles. Moreover, this strategy increases the electrical conductivity of the fabric by a factor of 4 without affecting the fabric’s air permeability (up to 400 mm s−1 at 50 Pa) and tensile properties (strains exceeding 100 %). Due to its wearability and multifunctionality, the fabric was designed as a synergistic monitoring system that includes facial respiration monitoring and knee joint motion monitoring for continuous monitoring of the athlete’s dynamic body condition. This thermoelectric fabric shows amazing potential for advanced smart textiles, soft robotics and human–computer interaction.

Review of community forestry monitoring systems in Nepal

A study was conducted to review the monitoring systems of various stakeholders involved in local level forest management in Nepal. The purpose of this review is to assess the current status of monitoring, identify and document lessons learnt, and explore gaps and opportunities for possible interventions.The study looked mainly at two aspects of existing monitoring systems and practices: a) self-monitoring practices of FUGs), and b) collaborative monitoring practices between FUGs and four different categories of stakeholders - Government, donor field projects, I/NGOs and civil society. A review of concepts related to participatory monitoring is made based on the literature, and aconceptual framework has been proposed for the review of monitoring practices in the context of community forestry in Nepal. Key elements used in the analysis include a) meanings and perceptions of monitoring, b) purpose, c) focus and scale, d) approach, processes and tools, e)contributions to adaptive management.

Collaborative Monitoring of Plant Biodiversity and Research on Sweet Acorn Oaks within Paths of Knowledge and Sustainability Education

Numerous studies have shown the multiple benefits of plant biodiversity and the crucial role of residents’ awareness of conservation and land management. Plant biodiversity was investigated in an analytical monitoring report conducted jointly with the local population engaged in livestock activities and young residents who were shifting toward hospitality and tourism. The study area, covering over 800 km2 in Sardinia, is half forested and attracts interest in its landscape. During our research, genotypes of Quercus ilex L., with sweet acorns rich in polyphenols, as the oldest ecosystem services in these rural communities, were discovered. Collaborative monitoring focused attention on the benefits of plants in different habitats as follows: 53% were known to livestock farmers as food for local breeds of goats and pigs, 15% were official, and 13% were endemic. They had also been used for human nutrition within the Mediterranean diet and attracted interest for their use in landscapes and gardens. This study analyzes numerical data from critical and educational perspectives. These data serve as indicators of ecosystem health for the purpose of sustainable management policies and attest to collaborative monitoring as a tool for analyzing human activities and the necessary balance between profit and biodiversity conservation, given the current challenging climate change conditions.

An overview of anthrax: a neglected zoonosis of the tropical region

Anthrax is a neglected bacterial zoonosis that can be highly contagious and potentially lethal. The disease, which mainly affects herbivorous ungulate animals, is caused by Bacillus anthracis, a rod-shaped, gram-positive bacterium. The pathogenicity depends on the production of toxins by Bacillus anthracis and the sensitivity of the host. The infection is transmitted to humans when they come in to contact with infected animals or their products. It is an important occupational risk to the abattoir workers. Clinical manifestations of disease in humans include different forms, including cutaneous, pulmonary and intestinal. The disease is widespread in African and Asian countries with frequent reports of cases. Lack of appropriate and effective control programs in these countries has led to increase in the number of human cases. Bacillus anthracis spores are extremely resistant to environmental conditions, and can persist for several decades in nature and, thus making the control or eradication of the disease difficult. Disease control involves managing contaminated animal products, avoiding contact with infected animals, livestock vaccination in endemic regions and routine surveillance of animals. The most effective and dependable way to control zoonotic diseases like anthrax is through collaborative monitoring programs including the "One Health" approach.

How well do collaboration quality estimation models generalize across authentic school contexts?

AbstractMultimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings from an evaluation of collaboration quality estimation models. We collected audio, video and log data from two different Estonian schools. These data were used in different combinations to build collaboration estimation models and then assessed across different subjects, different types of activities (collaborative‐writing, group‐discussion) and different schools. Our results suggest that the automated collaboration model can generalize to the context of different schools but with a 25% degradation in balanced accuracy (from 82% to 57%). Moreover, the results also indicate that multimodality brings more performance improvement in the case of group‐discussion‐based activities than collaborative‐writing‐based activities. Further, our results suggest that the video data could be an alternative for understanding collaboration in authentic settings where higher‐quality audio data cannot be collected due to contextual factors. The findings have implications for building automated collaboration estimation systems to assist teachers with monitoring their collaborative classrooms. Practitioners notesWhat is already known about this topic Multimodal learning analytics researchers have established several features as potential indicators for collaboration quality, e.g., speaking time or joint visual attention. The current state of the art has shown the feasibility of building automated collaboration quality models. Recent research has provided preliminary evidence of the generalizability of developed automated models across contexts different in terms of given task and subject. What does this paper add This paper offers collaboration indicators for different types of collaborative learning activities in authentic classroom settings. The paper includes a systematic investigation into collaboration quality automated model's generalizability across different tasks, types of tasks and schools. This paper also offers a comparison between different modalities' potential to estimate collaboration quality in authentic settings. Implications for practice The findings inform the development of automated collaboration monitoring systems for authentic classroom settings. This paper provides evidence on across‐school generalizability capabilities of collaboration quality estimation models.

Prediction Parameters for Mining Subsidence Based on Interferometric Synthetic Aperture Radar and Unmanned Aerial Vehicle Collaborative Monitoring

Coal mining induces surface subsidence, making rapid and precise monitoring of this subsidence a key area of current research. To address the limitations of D-InSAR technology in capturing large-gradient deformations in the central subsidence basin and the challenges facing UAVs in accurately monitoring small deformations at the basin’s edge, we propose a method for inverting the expected parameters of surface subsidence by synergistically integrating InSAR and UAV monitoring. We determined the cumulative subsidence of monitoring points along the dip and strike observation line of the Banji 110,801 working face between 10 April 2021 and 28 June 2022, employing D-InSAR and UAV techniques. By leveraging the complementary strengths of both monitoring techniques, we fused the two types of monitoring data and verified the error of the fusion data to be within 10 cm through leveling data verification. Simulation experiments utilizing the probability integration method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm confirmed that the 10 cm data source error remains within the required limits for probability integration parameter inversion. Finally, the BFGS algorithm was employed to invert the parameters of the probability integration method based on the fusion data results. Subsequently, these inversion parameters were used to predict the subsidence at the monitoring point and were compared with the level measured data. The results demonstrate that the use of collaborative InSAR and UAV monitoring technology for inverting the expected parameters of surface subsidence in the mining area yields superior results, aligning with the actual patterns of ground surface movement and deformation. This study addresses the global need for unmanned monitoring of mining-related subsidence. It employs InSAR and UAV technologies in a synergistic approach to monitor surface subsidence in mining regions. This approach harnesses the strengths of multiple data sources and presents a novel concept for the unmanned monitoring of surface subsidence in mining areas, contributing to environmental protection efforts.

A Distributed Adaptive Monitoring Method for Performance Indicator in Large-Scale Dynamic Process

The dynamic time-varying characteristic has brought great challenges to the plant-wide process monitoring. In this paper, a distributed adaptive principal component regression algorithm is proposed for the online indicator monitoring of large-scale dynamic process. Firstly, the distributed data subblocks are constructed according to the process operation units. In each subblock, an adaptive re-sampling method based on the subblock data and plant-wide data is presented to construct the modeling sample sets, which can extract the process local and global information simultaneously. Afterwards, the indicator-related feature is extracted, and the Bayesian method is used to integrate the subblock monitoring results. Through the collaborative monitoring of the process local and global feature spaces, a refined monitoring decision can be obtained. Finally, a numerical example and Tennessee Eastman process are used to illustrate the effectiveness of the proposed method.

Optimizing the Monitoring of Urban Fruit-Bearing Flora with Citizen Science: An Overview of the Pomar Urbano Initiative

The "Pomar Urbano" (Urban Orchard) project focuses on the collaborative monitoring of fruit-bearing plant species in urban areas throughout Brazil. The project collected a list of 411 fruit-bearing plant species (Soares et al. 2023), both native and exotic varieties found in Brazil. This list was selected from two main sources: the book Brazilian Fruits and Cultivated Exotics (Lorenzi et al. 2006) and the book series Plants for the Future, which includes volumes specifically dedicated to species of economic value in different regions of Brazil, namely the South (Coradin et al. 2011), Midwest (Vieira et al. 2016), Northeast (Coradin et al. 2018) and North (Coradin et al. 2022). To ensure broad geographic coverage, the project spans all 27 state capitals of Brazil. The data collection process relies on the iNaturalist Umbrella and Collection projects. Each state capital has a single collection project, including the fruit-bearing plant species list, and the locality restriction to that specific city. For example, the collection project Pomar Paulistano gathers data from the city of São Paulo. The Umbrella Project Urban Orchard was set to track data from all 27 collection projects. We firmly believe that these fruit-bearing plant species possess multifaceted value that extends beyond mere consumption. As such, we have assembled a dynamic and multidisciplinary team comprising professionals from various institutions across Brazil in a collaborative effort that encompasses different dimensions of biodiversity value exploration and monitoring, especially phenological data. One facet of our team is focused on creating products inspired by the diverse array of Brazilian fruit-bearing plants. Their work spans across sectors of the creative industry, including fashion, painting, and graphic design to infuse these natural elements into innovative and sustainable designs (Fig. 1 and Fig. 2). A group of nutrition and health scientists in conjunction with communication and marketing professionals is working to produce engaging media content centered around food recipes that incorporate Brazilian fruits (Fig. 3). These recipes primarily feature the fruit-bearing plants most frequently observed on iNaturalist in the city of São Paulo, allowing us to showcase the local biodiversity while promoting culinary diversity. Some of these recipes are based on the book Brazilian Biodiversity: Flavors and Aromas (Santiago and Coradin 2018). This book is an extensive compendium of food recipes that use fruits derived from native Brazilian species.

Research on the method of digital twin operation and maintenance platform for intelligent early warning of wind turbine tower

Wind power generators have a complex structure and operate in harsh environments, where working conditions are highly variable. As a result, the operation and maintenance of wind turbines face numerous challenges. In response to the need for the development of wind power operation and maintenance informatization, it is necessary to satisfy the requirements for multi-party collaborative monitoring to ensure the long-term safe and reliable operation of wind turbines. In this paper,we proposed a method for building an intelligent early-warning digital twin platform focused on the simulation of wind turbines and tower components. The platform construction method proposed in this article is based on the Web and from the perspective of intelligent operation and maintenance of wind turbines. It establishes a warning model for tower agent simulation and vibration signal time series prediction. The tower mechanism model is established based on the operating data set of a 4MW wind turbine at Shanghai Electric. Different physical responses of the tower under different wind speeds are simulated, and an agent model using LSTM and decision tree models is established for predictive analysis. To account for uncertainty, a Bayesian-LSTM model is established to warn against predictive errors. Finally, a data-driven digital twin wind turbine platform is achieved on the Web.