Environmental Data Collection Research Articles

Mobile network and computer software test path selection application in environmental data collection simulation

Mobile network and computer software test path selection application in environmental data collection simulation

Characterizing juvenile blacktip shark (Carcharhinus limbatus) nursery areas in the Galapagos: new methods and understandings.

New approaches to abundance surveying utilizing unoccupied aerial vehicles (UAVs) are proving to be effective tools in marine and terrestrial environments. We explored UAV efficacy for surveys in the Galapagos Marine Reserve (GMR), where relative abundance patterns of juvenile sharks and subsequent classifications of putative nursery areas based on environmental drivers are lacking. The UAV method allowed greater temporal and spatial coverage. We expanded classification of shark nursery habitat through monthly drone surveys and environmental data collection at 14 sites around San Cristobal Island (GMR) from November 2018 to July 2019. In the period of surveying, 56 flights identified 453 juvenile Carcharhinus limbatus (blacktip shark). Classification of nurseries followed three criteria, necessitating higher density, short-term residency, and annual site fidelity in target locations. We developed preliminary generalized linear models to elucidate potential environmental parameters influencing the perceived abundance and habitat preference of juveniles. Four sites were identified as either potential nurseries or nursery areas for the target species based on previous excursions. An averaged model was subsequently created from the models found to best explain deviance patterns (R2 = 0.10-0.44) in perceived shark abundance and habitat preference. Relative variable importance (RVI) values further clarified the parameters most associated with higher juvenile presence. This approach provides a systematic method of abundance surveying while simultaneously beginning the process of defining when and where we expect to find higher abundance through environmental modelling of most influential parameters to perceived abundance in this environment.

APPLICABILITY OF GAMIFICATION TO THE TEACHING AND LEARNING PROCESS

This research aimed to investigate interactive learning experiences through gamification, evaluating their impact on students’ motivation and academic performance. The methodology involved the analysis of practical cases and the collection of data in educational environments that implemented gamified practices, allowing an in-depth understanding of the dynamics involved. The results indicated that gamification significantly increases student engagement, promotes collaboration and contributes to the development of socio-emotional skills, in addition to improving content retention. The analysis of the data revealed that, although gamification offers many benefits, its effectiveness depends on careful implementation and adaptation to students’ needs, in addition to overcoming challenges such as inequality in access to technology. The conclusion emphasizes the importance of adopting these methodologies on a large scale, highlighting that, by transforming the learning environment into a more interactive and meaningful experience, gamification can be a valuable tool to modernize education and meet the demands of the 21st century.

Improving Communication Networks to Transfer Data in Real Time for Environmental Monitoring and Data Collection

Integrated communication networks (CN) have proven successful in tracking environmental activities, wherein several sensors are installed throughout diverse surroundings to gather data or observe certain events. CNs, comprising several interacting detectors, have proven effective in various applications by transmitting data via diverse transmission methods inside the communication system. The erratic and constantly changing surroundings necessitate conventional CNs to engage in frequent conversations to disseminate the latest data, potentially incurring substantial connection expenses through joint data gathering and dissemination. High-frequency communications are prone to failure due to the extensive distance of data transfer. This research presents a unique methodology for multi-sensor environmental monitoring networks utilizing autonomous systems. The transmission system can mitigate elevated communication costs and Single Point of Failing (SPOF) challenges by employing a decentralized method that facilitates in-network processing. The methodology employs Boolean systems, enabling a straightforward verification process while preserving essential details about the dynamics of the communication system. The methodology further simplifies the data collection process and employs a Reinforcement Learning (RL) technique to forecast future events inside the surroundings by recognizing patterns.

Temperature and solar irradiation effects on secondary metabolism during ripening of field-grown everbearing strawberries

The garden strawberry (Fragaria x ananassa Duch.) is cultivated and consumed worldwide because of the pleasant flavor and health-promoting phytochemicals of its false fruits. Monocrop cultivars produce fully ripe strawberries in about one month post-anthesis throughout the spring, while everbearing cultivars undergo additional strawberry production in autumn. In this work, we evaluated the impact of different season-dependent environmental conditions on the ripening program of an everbearing field-gown strawberry variety from autumn 2015 to spring 2016. We combined ad hoc sampling and environmental data collection with LC-MS-based untargeted metabolomics to dissect the effects of cumulative temperature and solar irradiation on fruit quality parameters and secondary metabolism during ripening. Different dynamics in specific sub-groups of metabolites were observed in strawberries experiencing distinct amounts of cumulative temperature and solar irradiation during spring and autumn. The integration of statistical analyses on collected data revealed that solar irradiation mainly affected fruit fresh weight and organic acid levels, whereas temperature had a more selective effect on the accumulation of specific flavonols, anthocyanins, and soluble sugar. These findings are of suitable interest to design further approaches for the study of the complex interactions among environmental conditions and ripening in strawberries grown in a real-world scenario.

Adaptive Path Planning for Subsurface Plume Tracing with an Autonomous Underwater Vehicle

Autonomous underwater vehicles (AUVs) have been increasingly applied in marine environmental monitoring. Their outstanding capability of performing tasks without human intervention makes them a popular tool for environmental data collection, especially in unknown and remote regions. This paper addresses the path planning problem when AUVs are used to perform plume source tracing in an unknown environment. The goal of path planning is to locate the plume source efficiently. The path planning approach is developed using the Double Deep Q-Network (DDQN) algorithm in the deep reinforcement learning (DRL) framework. The AUV gains knowledge by interacting with the environment, and the optimal direction is extracted from the mapping obtained by a deep neural network. The proposed approach was tested by numerical simulation and on a real ground vehicle. In the numerical simulation, several initial sampling strategies were compared on the basis of survey efficiency. The results show that direct learning based on the interaction with the environment could be an appropriate survey strategy for plume source tracing problems. The comparison with the canonical lawnmower path used in practice showed that path planning using DRL algorithms could be potentially promising for large-scale environment exploration.

Landscape of racial and ethnic health disparities in the All of Us Research Program.

The All of Us Research Program ("All of Us") is an initiative led by the National Institutes of Health whose goal is to advance research on personalized medicine and health equity through the collection of genetic, environmental, demographic, and health data from volunteer participants who reside in the USA. The program's emphasis on recruiting a diverse participant cohort makes "All of Us" an effective platform for investigating health disparities. In this work, we analyzed participant electronic health record (EHR) data to identify the diseases and disease categories in the "All of Us" cohort for which racial and ethnic prevalence disparities can be observed. In conjunction with these analyses, we developed the US Health Disparities Browser as an interactive web application that enables users to visualize differences in race- and ethnic-group-specific prevalence estimates for 1755 different diseases: https://usdisparities.biosci.gatech.edu/. The web application features a catalog of all diseases represented in the browser, which can be sorted by overall prevalence as well as the variance in prevalence across racial and ethnic groups. The analyses outlined here provide details on the nature and extent of racial and ethnic health disparities in the "All of Us" participant cohort, and the accompanying browser can serve as a resource through which researchers can explore these disparities Database URL: https://usdisparities.biosci.gatech.edu.

Global Water Challenges in Sub-Saharan Africa and how to strengthen science-policy dialogues on transboundary governance and cooperation

The United Nations Water Conference 2023 highlighted the need for concrete actions to boost integrated water resources management for achieving the Sustainable Development Goals and called for strategies to enhance cooperation among stakeholders. Technical cooperation between countries and institutions in transboundary systems, e.g., on environmental data collection, is an effective way to promote international diplomacy and prevent disputes between riparian states. Still, establishing collaborations to inform bilateral dialogues on the identification of environmental challenges, their causes, and development priorities may be a difficult task in itself. This is particularly true in the African context because of limited resources and lack of data. In this paper, we analyse the case of nine transboundary river basins in Sub-Saharan Africa to identify which water-management challenges are perceived as most important by the different riparian countries from a policy and scientific perspective. Our insights are based on the most up-to-date scientific papers, open access reports and technical literature, river basin authority’s strategy papers, projects’ summary reports, and national policy documents. We also complement these sources with the pieces of information we gained through collaborations with regional and local experts, and management bodies (such as river basin authorities). We highlight the current water-related conflicts and the gap between the priorities identified by the scientific community and different riparian countries on how to tackle hydro-climatic change and improve food and energy security, human and environmental health. Based on our experience, we discuss some keys to building trust among stakeholders, strengthening cooperation, and identifying shared water-governance measures in transboundary river basins. They are: (i) connect science and policy to provide sound knowledge for the right questions, (ii) value local knowledge and exploit the complementarity of different perspectives, (iii) consider multiple spatial scales and multi-level stakeholders to leave no one behind, (iv) promote a culture which values trade-offs and handles complexity, and (v) co-create data and knowledge to facilitate stakeholder dialogue from problem definition to intervention identification.

Fisheries independent surveys in a new era of offshore wind energy development

Abstract Fisheries independent surveys require rethinking because of increasing spatial restrictions and interactions with offshore wind energy development (OWD). Fisheries, protected species, and environmental data collections have been conducted by scientific institutions to meet societal demands for food security, conservation, and other marine uses. These data collections provide information on key resource measures, essential for fisheries, protected species, and ecosystem management. With the increase in pace and magnitude of OWD's industrialization of marine waters, disruptions in these long-term time series can be expected. These disruptions will impact the ability to support current and future management goals and objectives. This paper presents an expert survey on the perceptions of OWD interactions with common survey designs and survey methodologies in Europe and the U.S., along with a selected sample of 75 fisheries independent surveys in the U.S. and Europe providing an initial assessment and description of potential impacts from OWD. About 72% of the surveys sampled record interactions with operational, planned or future OWD. Four case studies demonstrate efforts to address these interactions within European regions that have operational OWD and the U.S. where development has just begun. Finally, we make recommendations for future research important to continue meaningful scientific-based management advice.

Personalized Weather Station

Abstract: This research outlines the conception, design, and implementation of a personalized weather monitoring system integrating multiple sensors for comprehensive environmental data collection. The project aims to create a sophisticated weather station utilizing the DHT11 sensor for temperature and humidity measurements, MQ135 sensor for gas detection, dust sensor for particulate matter assessment, and rain sensor for precipitation detection. The system's architecture incorporates Esp32-based data acquisition, employing sensor interfacing and data processing to obtain real-time environmental parameters. The project's significance lies in providing localized, accurate, and immediate weather insights beyond the capabilities of conventional smartphone applications.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®.

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

The Impacts of Sunlight on the Lichen Scots Pine Forest Community

The habitat of the lichen Scots pine forest is currently one of the communities that requires attention and active protection due to its rapid disappearance. In our study, we identified factors that can be modified to preserve this habitat. The primary focus of our research was on the significance of tree height and sunlight in fostering the development of a lichen-rich layer within the lichen Scots pine forest. Additionally, we investigated the environmental factors influencing the presence of specific species of lichens and bryophytes, including those that form communities and those that displace species characteristic of the lichen Scots pine forest community. Our study was conducted in Bory Tucholskie National Park (N-W Poland) using 20 experimental sites and 248 point-frame plots from spring 2021 to winter 2022. Fieldwork involved species surveys, measurements of photosynthetic activity in lichens and bryophytes, and collection of environmental data. Through parametric testing, modeling, and mapping, our main findings confirmed that light availability, influenced by tree height, is a critical factor in maintaining a well-preserved lichen-rich layer and facilitating habitat reintroduction.

Systematic literature review: Digital twins' role in enhancing security for Industry 4.0 applications

AbstractConnectivity and data exchange are key features of Industry 4.0. In this paradigm, (Industrial) Internet of Things ((I)IoT) devices are a vital component facilitating the collection and transmission of environmental data from the physical system to the central station for processing and analysis (digital twin [DT]). However, although (I)IoT devices play a critical role in this process, they are not inherently equipped to run strong encryption mechanisms to secure the data they transmit over wired or wireless channels. This research aims to explore the potential of DTs in securing Industry 4.0 applications and the security mechanism employed to ensure confidentiality, integrity, and authentication of data communicated between (I)IoT and DT through a systematic literature review (SLR). This SLR, based on the analysis of 67 papers published between 2018 and 2023, underscores the evolving significance of DT technology, particularly within the ambit of Industry 4.0. The findings illuminate the pervasive influence of DT technology across multiple industrial sectors. The result SLR revealed that DT is growing and being widely adopted as a security tool particularly in Industry 4.0 using enabling technology like machine learning, data analytics, blockchain, and 5G networks to provide security solutions such as intrusion detection, vulnerability assessment, cyber range, and threat intelligence.

Application of non-lethal bioSPME-LC-MS/MS for the detection of human pharmaceuticals in soft corals: A survey at the North Nilandhe atoll (Maldives)

At present the information regarding the occurrence of human pharmaceuticals (PhaCs) in coral reefs and their potential impacts on the associated fauna is limited. To optimize the collection of data in these delicate environments, we employed a solid-phase microextraction (bioSPME) and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) procedure that enabled in vivo determinations in soft corals. Specifically, we researched the antibiotics Ofloxacin Sulfamethoxazole and Clarithromycin, the anti-inflammatory Diclofenac Propyphenazone Ketoprofen and Amisulpride, the neuroactive compounds Gabapentin-lactam, the beta-blocker Metoprolol and the antiepileptic Carbamazepine. Reproducibility was between 2.1% and 9.9% and method detection limits LODs) were between 0.2 and 1.6 ng/g and LOQs between 0.8 and 5.4 mg/g. The method was then applied to establish a baseline for the occurrence of these compounds in the Maldivian archipelago. Colonies of Sarcophyton sp. and Sinularia sp. were sampled along an inner-outer reef transect. Five of the ten targeted PhaCs were identified, and 40% of the surveyed coral colonies showed the occurrence of at least one of the selected compounds. The highest concentrations were found inside the atoll rim. Oxoflacin (9.5 ± 3.9 ng/g) and Ketoprofen (4.5 ± 2.3 ng/g) were the compounds with the highest average concentrations. Outside the atoll rim, only one sample showed contamination levels above the detection limit. No significant differences were highlighted among the two surveyed soft coral species, both in terms of average concentrations and bioconcentration factors (BCFs).

Advancing Sea Turtle Monitoring at Nesting and Near Shore Habitats with UAVs, Data Loggers, and State of the Art Technologies

In the face of environmental change, high-quality and fine-scale information is essential in order to monitor the highly dynamic environments on land and sea. While traditional approaches to data collection face a number of practical limitations, advanced technologies could supplement and further improve our efforts. Taking sea turtles as a modeling organism, we present a novel methodological framework for monitoring species by means of advanced technologies, including Unmanned Aerial Vehicles coupled with image and temperature sensors. Diverse monitoring protocols were refined through pilot studies conducted in both terrestrial and nearshore sea turtle habitats. Our approach focuses on the collection of information for critical biological parameters concerning species reproduction and habitat use, following the complex life cycle of the species. Apart from biological information, our framework encompasses also the collection of information on crucial environmental factors that might be changing due to current and future human-derived pressures, such as beach erosion and temperature profile, as well as highly important human activities such as recreational use within nesting beaches that could undermine habitat quality for the species. This holistic and standardized approach to monitoring using advanced technologies could foster our capacity for conservation, resolving difficulties previously addressed and improving the collection of biological and environmental data in the frame of an adaptive management scheme.

An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks.

The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL's effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.

Different Scenarios of Autonomous Operation of an Environmental Sensor Node Using a Piezoelectric-Vibration-Based Energy Harvester.

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node's power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery's lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems.

Design of cloud data storage security and financial risk control management early warning system based on sensor networks

The wide application of sensor networks has brought great challenges to cloud data storage, such as transmission security, data privacy protection and financial risk control. In order to deal with these challenges, the system establishes a comprehensive management early warning system to ensure the data security and financial risk control of the sensor network. The system integrates sensor network technology and cloud data storage technology, and stores data in the cloud through the collection and transmission of environmental data by sensor nodes. In order to ensure the security of the stored process, the system adopts a series of security mechanisms, such as data encryption, access control and identity authentication, and also introduces the financial risk control module, which helps users to warn and manage financial risks through real-time monitoring and analysis of data in the sensor network. The system provides a user-friendly management interface, allowing users to easily configure and monitor the operating state of the sensor network, and supports flexible expansion and customization to adapt to the needs of different scenarios. Through the application of this system, users can effectively manage and control the security and financial risks of sensor networks in cloud data storage, and improve the efficiency and reliability of data storage management.

Carbon Neutrality in the Building Sector of the Global South—A Review of Barriers and Transformations

This work provides a review of economic, technical, sociocultural, political, and technological barriers that impede carbon neutrality in the building sector of countries in the Global South. These barriers include limitations in public and professional awareness, knowledge, and skills in the construction industry, the lack of ambitious energy codes and green building rating systems, the lack of green financing schemes and investment, costs of materials and technology, and limitations in regulations. Finally, this article recommends five transformations to address the critical barriers and enable net-zero emission status in the building sector of countries in the Global South. These transformations include environmental data collection, stringent building energy codes, and green building system certifications, lifecycle-based thinking and circular design, education enhancement and workforce development, and net-zero emission business practices.

GeoFOR: A collaborative forensic taphonomy database for estimating the postmortem interval

Accurately assessing the postmortem interval (PMI), or the time since death, remains elusive within forensic science research and application. This paper introduces geoFOR, a web-based collaborative application that utilizes ArcGIS and machine learning to deliver improved PMI predictions. The geoFOR application provides a standardized, collaborative forensic taphonomy database that gives practitioners a readily available tool to enter case information that automates the collection of environmental data and delivers a PMI prediction using statistically robust methods. After case submission, the cross-validating machine learning PMI predictive model results in a R² value of 0.82. Contributors receive a predicted PMI with an 80% confidence interval. The geoFOR database currently contains 2529 entries from across the U.S. and includes cases from medicolegal investigations and longitudinal studies from human decomposition facilities. We present the overall findings of the data collected so far and compare results from medicolegal cases and longitudinal studies to highlight previously poorly understood limitations involved in the difficult task of PMI estimation. This novel approach for building a reference dataset of human decomposition is forensically and geographically representative of the realities in which human remains are discovered which allows for continual improvement of PMI estimations as more data is captured. It is our goal that the geoFOR data repository follow the principles of Open Science and be made available to forensic researchers to test, refine, and improve PMI models. Mass collaboration and data sharing can ultimately address enduring issues associated with accurately estimating the PMI within medicolegal death investigations.