Analysis Of Environmental Data Research Articles

GeoAR: a calibration method for Geographic-Aware Augmented Reality

The applicability of augmented reality (AR) as a tool for geospatial data analysis and urban environment interaction relies on developing robust and accurate systems capable of aligning the virtual reference frame with the geographic one. In this article, we introduce our work toward the conceptualization and realization of Geographic-Aware Augmented Reality (GeoAR), including an evaluated framework for the automatic registration of georeferenced AR content. The proposed framework uses a novel calibration method that enables highly accurate placement of augmentations at their assigned geographic coordinate. Moreover, it introduces four calibration approaches suitable for different user needs. The framework was evaluated to assess the robustness of the augmentation’s positional accuracy in three areas with different environmental characteristics, using references up to 50 m away while the user moves around. The results demonstrate that this framework supports novel outdoor AR applications, extending the possibilities in research and urban applications.

Planning for Urban Sustainability through Residents’ Wellbeing: The Effects of Nature Interactions, Social Capital, and Socio-Demographic Factors

The COVID-19 pandemic provided a unique opportunity to examine how varied environmental and social factors in urban environments affect human wellbeing, which is an important dimension of urban sustainability. Past research has focused on individual dimensions of health and isolated nature interactions, limiting knowledge about how different environmental and social factors affect distinct aspects of residents’ wellbeing. Through quantitative analyses of social survey and environmental data in metropolitan Phoenix, Arizona (USA), we explored how distinct nature recreation activities—along with nature satisfaction and social capital—affected residents’ subjective, mental, and physical wellbeing across diverse neighborhoods during the COVID-19 pandemic, controlling for socio-demographics. Results reveal how distinct drivers shape different dimensions of wellbeing. Perceived social and environmental attributes of neighborhoods and proximity to nature preserves were associated with subjective wellbeing, while park visitation was linked to physical health. Nature interactions largely were unrelated to mental health. Changes in nature recreation during the COVID-19 pandemic did not significantly impact any dimension of wellbeing. Our research suggests that multiple local environmental and social features should be considered when designing healthy communities for urban sustainability.

IMMERSIVE ANALYTICS

Immersive Analytics focuses on the benefits and challenges of using immersive environments for data analysis, and developing designs to improve efficiency. Although immersive technologies are widely available, practical solutions have not gained widespread acceptance in real- world applications. Research in this field focuses on abstract 3D visualization, immersive environments, paper sampling and use case evaluation. 03 Related Works • Brooks early review of VR applications found it effective in specific domains like flight simulators, automotive engineering, and astronaut training. • Van Dam et al. highlighted VR applications for SciVis, benefiting archaeology and medical fields. • Laha and Bowman reviewed VR techniques for visualizing volume data, highlighting the need for controlled experiments to explore individual components of immersion. • Reda et al. summarized research for hybrid reality environments like the CAVE2, emphasizing the possibility of collaborative data analysis. • Brath collected evidence that 3D visualizations offer advantages beyond 2D, focusing on immersive displays. 05 Result • Overall, immersive analytics can lead to better data comprehension, better decision-making, more engagement and teamwork, effective big data exploration, creative data visualization methods, non-technical users' empowerment, and applications in a variety of fields. • These results add to the increasing importance of immersive analytics as an efficient tool for decision support and data analysis. Introduction • To facilitate data-driven analytical reasoning, immersive analytics leverages interactive technology like as virtual reality glasses, big flat screen displays, and even the internet of things. • Immersion refers to an experience that creates a genuine sense of presence in a virtual environment. • A person experiences a shift in awareness from their immediate real environment to another reality. • Immersion analytics is still a relatively new field that has mostly been studied in use cases and workshops. • The notion remains extremely difficult, even within the small community of immersive analytics practitioners. Methodology 4.1 Immersive environments • The study focuses on immersive environments leading to a mixed reality experience. • Abstract 3D visualizations must be presented in a mixed or VR environment where hardware and user interact closely. 4.2 Abstract 3D visualizations • Data that lacks a physical representation or intrinsic spatial organization is referred to as abstract data. • In visualization, abstraction is obtained by using colors and shapes that are not directly associated with the object. 4.3 Paper sampling • Paper sampling in immersive analytics refers to the process of selecting and gathering relevant research papers and publications related to immersive analytics. Conclusion and future scope • In conclusion, immersive analytics has the potential to completely transform how we work with data by facilitating deeper understanding, better decision-making, and improved teamwork. • Immersion analytics will develop further and contribute significantly to data analysis and decision support in the future by tackling issues and seizing opportunities in technological innovation, domain-specific applications, ethical issues, and user experience design.

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-Geographic Information System Decision Support System (Web-GIS DSS) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and Organization for Economic Corporation and Development (OECD) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, Iran.The developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

Indoor Light Environment Factors That Affect the Psychological Satisfaction of Occupants in Office Facilities

Recently, human well-being has emerged as a pivotal concern that affects not only quality of life but also social and economic dimensions. Lighting in office spaces is crucial for the health and cognitive function of occupants, and various methods are used to assess it. However, there is still a lack of research investigating the relationship between lighting satisfaction and its key factors in diverse spaces. This study focused on the office light environment, using quantitative and qualitative data analysis to understand occupants’ satisfaction and the factors influenced by lighting characteristics. According to the results, occupant satisfaction was higher in light environments with illuminance levels exceeding the appropriate illuminance standard. Furthermore, the influx of daylight and its influencing factors, such as daylight exposure and window size, played a significant role in enhancing satisfaction. However, while daylight was a primary source of glare, its mitigation was not solely reliant on installations, such as blinds; architectural designs, including space arrangement, were also crucial in addressing it. Furthermore, the perception of the lighting environment varied based on work behaviors and spatial arrangements, influencing satisfaction with the lighting conditions. Therefore, a comprehensive approach that considers lighting elements, human behavior, and architectural design is essential in creating a lighting environment for office occupants.

A New Generation of Collaborative Immersive Analytics on the Web: Open-Source Services to Capture, Process and Inspect Users’ Sessions in 3D Environments

Recording large amounts of users’ sessions performed through 3D applications may provide crucial insights into interaction patterns. Such data can be captured from interactive experiences in public exhibits, remote motion tracking equipment, immersive XR devices, lab installations or online web applications. Immersive analytics (IA) deals with the benefits and challenges of using immersive environments for data analysis and related design solutions to improve the quality and efficiency of the analysis process. Today, web technologies allow us to craft complex applications accessible through common browsers, and APIs like WebXR allow us to interact with and explore virtual 3D environments using immersive devices. These technologies can be used to access rich, immersive spaces but present new challenges related to performance, network bottlenecks and interface design. WebXR IA tools are still quite new in the literature: they present several challenges and leave quite unexplored the possibility of synchronous collaborative inspection. The opportunity to share the virtual space with remote analysts in fact improves sense-making tasks and offers new ways to discuss interaction patterns together, while inspecting captured records or data aggregates. Furthermore, with proper collaborative approaches, analysts are able to share machine learning (ML) pipelines and constructively discuss the outcomes and insights through tailored data visualization, directly inside immersive 3D spaces, using a web browser. Under the H2IOSC project, we present the first results of an open-source pipeline involving tools and services aimed at capturing, processing and inspecting interactive sessions collaboratively in WebXR with other analysts. The modular pipeline can be easily deployed in research infrastructures (RIs), remote dedicated hubs or local scenarios. The developed WebXR immersive analytics tool specifically offers advanced features for volumetric data inspection, query, annotation and discovery, alongside spatial interfaces. We assess the pipeline through users’ sessions captured during two remote public exhibits, by a WebXR application presenting generative AI content to visitors. We deployed the pipeline to assess the different services and to better understand how people interact with generative AI environments. The obtained results can be easily adopted for a multitude of case studies, interactive applications, remote equipment or online applications, to support or accelerate the detection of interaction patterns among remote analysts collaborating in the same 3D space.

Factors Associated With Uptake of an Open Access Health Center–Based Mobile Produce Market: A Case for Expanded Eligibility

BackgroundHealth care–based food assistance programs show promise but are underutilized. Strict eligibility requirements and program scheduling may dampen reach and outcomes. ObjectiveTo explore factors associated with uptake of a health center–based mobile produce market with no eligibility requirements and few barriers to entry. DesignA cross-sectional analysis of medical record, sociodemographic, environmental, and market attendance data was used. Participants/SettingThe study sample consisted of 3071 adults (18+ years) who were patients of an urban health center in eastern Massachusetts and registered for the mobile market during the study period of August 2016 to February 2020. Main Outcome MeasuresThe main outcome measure was monthly market attendance over the study period. Statistical AnalysesT-tests and χ2 tests were used to compare market users and never-users. Multiple logistic regression was used to analyze variables associated with market attendance each month. ResultsIn multiple variable analyses, Supplemental Nutrition Assistance Program (SNAP) enrollment was associated with slightly less frequent monthly market use (odds ratio [OR], 0.989; 95% CI, 0.984–0.994). Day-of, on-site market registration was associated with more frequent monthly use than self-registration on nonmarket days (OR, 1.08; 95% CI, 1.07–1.08). Having a psychiatric or substance use disorder diagnosis was associated with slightly less frequent market attendance (OR, 0.99; 95% CI, 0.98–0.99; and OR, 0.96; 95% CI, 0.95–0.97, respectively) compared with registrants without these diagnoses. ConclusionsIndividual, community-level, and organizational factors are associated with uptake of a free mobile produce market and should be considered when designing programs.

Incorporating temporal multi-head self-attention convolutional networks and LightGBM for indoor air quality prediction

Friction between subway wheels and tracks and inadequate combustion of fuel are causes of respirable particulate matter. Restricted underground ventilation and high population density make it difficult for particulate matter to dissipate, posing a threat to human health. An effective data-driven model for indoor pollutant prediction can enhance preparedness for high pollution situations. This study introduces a feature extraction method that combines kernel principal component analysis with max-relevance and min-redundancy algorithm. The temporal convolutional network, enhanced with a multi-head self-attention mechanism, adeptly captures time series features and effectively manages attention weight allocation. Additionally, the incorporation of the light gradient boosting machine method enhances overall efficiency. The proposed framework for PM2.5 concentration prediction was employed to a high-traffic subway station in Seoul. In the test set, the model demonstrated strong performance with evaluation metrics including an R2 value of 0.92, RMSE of 6.02 μg/m3, MAE of 4.36, and MAPE of 20.58 μg/m3. Compared to the conventional LSTM, the proposed method reduces the RMSE by 20.5% and the MAPE by 49.05%. Notably, the model we propose demonstrates superior capabilities in managing large datasets and offers enhanced predictive accuracy compared to baseline models. It effectively addresses the limitations observed in models like LSTM, which often struggle with adequately capturing feature information, and overcomes the generalization weaknesses inherent in models such as the Transformer. This advancement significantly boosts the efficiency of environmental monitoring and fosters both automation and intelligence in the analysis of environmental data.

Integrating IoT Sensors, Cloud AI, and Satellite Broadband for Enhanced ESG Governance in Smart Cities: A Tripartite Approach to Real-Time Environmental Monitoring

Urbanization is accelerating at an unprecedented rate, pushing the limits of environmental sustainability and demanding innovative solutions for effective management. Traditional methods for monitoring the environment fall short, lacking the scalability, immediacy, and comprehensive coverage required for modern urban settings. Our paper introduces an innovative solution to bridge these gaps, harnessing the power of Internet of Things (IoT) sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband. This threefold integration forms a cutting-edge approach to urban environmental governance. It enables the analysis of environmental data in real time, offers predictive insights, and achieves wide-ranging coverage. The system equips city leaders, policymakers, and citizens with precise, actionable information, fostering decisions that align with Environmental Social Governance (ESG) goals. More than just improving urban life and sustainability, our approach makes environmental data accessible to all, promoting a collaborative and inclusive model of urban environmental management.

A crowdsensing-based framework for sound and vibration data analysis in smart urban environments

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

A new three-parameter lifetime distribution for environmental data analysis: the Harris extended modified Lindley distribution

A new three-parameter lifetime distribution for environmental data analysis: the Harris extended modified Lindley distribution

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well.During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI predictionbased on satellite images. Our hybrid model consists of two parts as follows:•The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation.•The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI.From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them.Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

Global environmental and toxicological impacts of polybrominated diphenyl ethers versus organophosphate esters: A comparative analysis and regrettable substitution dilemma

The prevalence of organophosphate esters (OPEs) in the global environment is increasing, which aligns with the decline in the usage of polybrominated diphenyl ethers (PBDEs). PBDEs, a category of flame retardants, were banned and classified as persistent organic pollutants (POPs) through the Stockholm Convention due to their toxic and persistent properties. Despite a lack of comprehensive understanding of their ecological and health consequences, OPEs were adopted as replacements for PBDEs. This research aims to offer a comparative assessment of PBDEs and OPEs in various domains, specifically focusing on their persistence, bioaccumulation, and toxicity (PBT) properties. This study explored physicochemical properties (such as molecular weight, octanol-water partition coefficient, octanol-air partition coefficient, Henry's law constant, and vapor pressures), environmental behaviors, global concentrations in environmental matrices (air, water, and soil), toxicities, bioaccumulation, and trophic transfer mechanisms of both groups of compounds. Based on the comparison and analysis of environmental and toxicological data, we evaluate whether OPEs represent another instance of regrettable substitution and global contamination as much as PBDEs. Our findings indicate that the physical and chemical characteristics, environmental behaviors, and global concentrations of PBDEs and OPEs, are similar and overlap in many instances. Notably, OPE concentrations have even surged by orders of several magnitude compared to PBDEs in certain pristine regions like the Arctic and Antarctic, implying long-range transport. In many instances, air and water concentrations of OPEs have been increased than PBDEs. While the bioaccumulation factors (BAFs) of PBDEs (ranging from 4.8 to 7.5) are slightly elevated compared to OPEs (−0.5 to 5.36) in aquatic environments, both groups of compounds exhibit BAF values beyond the threshold of 5000 L/kg (log10 BAF > 3.7). Similarly, the trophic magnification factors (TMFs) for PBDEs (ranging from 0.39 to 4.44) slightly surpass those for OPEs (ranging from 1.06 to 3.5) in all cases. Metabolic biotransformation rates (LogKM) and hydrophobicity are potentially major factors deciding their trophic magnification potential. However, many compounds of PBDEs and OPEs show TMF values higher than 1, indicating biomagnification potential. Collectively, all data suggest that PBDEs and OPEs have the potential to bioaccumulate and transfer through the food chain. OPEs and PBDEs present a myriad of toxicity endpoints, with notable overlaps encompassing reproductive issues, oxidative stress, developmental defects, liver dysfunction, DNA damage, neurological toxicity, reproductive anomalies, carcinogenic effects, and behavior changes. Based on our investigation and comparative analysis, we conclude that substituting PBDEs with OPEs is regrettable based on PBT properties, underscoring the urgency for policy reforms and effective management strategies. Addressing this predicament before an exacerbation of global contamination is imperative.

Influence of mother’s genotype on the concentration and variability of triiodothyro- nine in landrace pigs

One of the leading roles in the hormonal system of the body of pigs in terms of functional significance is played by the thyroid gland, which, through the hormone triiodothyronine, has a complex effect on all systems, organs and physiological processes while controlling the metabolism and energy in the animal’s body. The impact of maternal genotype on the concentration and variability of triiodothyronine in complete siblings of Landrace pigs was studied. For the study, blood serum samples were taken from 33 pigs using the vacuum method from the jugular vein. Blood samples were taken according to generally accepted practices. The studies were carried out in the laboratory of the biochemistry of the Novosibirsk State Agrarian University using the technique of competitive enzyme immunoassay with a set of reagents “Steroid ELISA-triiodothyronine” to quantify the concentration of triiodothyronine in blood serum. Enzyme immunoassay was performed on a Thermo Scientific Multiskan FC analyser. In pig breeding, the content of heavy metals in water, soil, feed, organs and tissues was constantly monitored. The concentration of chemical elements did not exceed the MPC level. Statistical processing of experimental data was carried out using standard methods of descriptive statistics and the statistical programming language or data analysis environment RStudio (using the functions: descrstats, summary, sd, read.table, write. table, aes). The strength of the influence of the genotype of mothers of the Landrace breed on the triiodothyronine content in complete siblings in the conditions of Western Siberia was established, which amounted to 4.6 %. In some mothers’ offspring, the triiodothyronine level was 1.9 and 1.8 times higher than in others. Two clusters of mothers were identified according to the concentration of triiodothyronine in complete siblings, which included three groups of mothers with an average level of 2.26 nmol/l and five groups with the attention of 1.30 nmol/l. The reference limits of the triiodothyronine level in Landrace pigs in the conditions of Western Siberia were established.

Interspecific features of copper accumulation and variability in the skeletal muscle of farm animals

The results of a study of copper levels in the skeletal muscles of cattle, sheep, pigs and yaks, carried out on clinically healthy animals raised in the Siberian region, are presented. The living conditions of the animals corresponded to the standard ones depending on the species. Elemental analysis of muscle tissue samples was performed using inductively coupled plasma atomic emission spectral analysis. Data processing was done using Microsoft Office Excel and the R programming language in the data analysis environment RStudio version 2023.03.1 (RStudio, PBC). In most cases, the distribution did not correspond to normal, and the variances were not homogeneous. Based on the average value and median, a descending ranked series of copper content in tissue was established: cattle < yaks < sheep < pigs, in numerical terms based on the median: 1.57: 1.29: 1.02: 1.0. The medians for copper in cattle, yaks, sheep and pigs were 1.40; 1.15; 0.024; 0.91; 0.89 mg/kg, respectively, reference intervals are 0.40–2.13, 0.30–2.16, 0.39–1.43 and 0.28–1.47 mg/kg. The most significant range of variability,based on the standard deviation and the ratio of extreme variants, is characteristic of cattle. Based on the Kruskal-Walli’s test, muscle copper accumulation differed significantly between species (H = 18.277, df = 3, p < 0.001). Pairwise comparison showed significant differences in the pairs “cattle – pigs”, “cattle – sheep” and “pigs – yaks”. Two clusters were identified based on the similarity of copper accumulation: cattle - yaks and sheep - pigs. The results can serve as an approximate physiological norm for the concentration of copper in the skeletal muscles of animals of different species and confirm the genotype’s influence on the organism’s cumulative abilities.

Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

Revision of Seriphium plumosum (Gnaphalieae: Asteraceae) in southern Africa with description of new species in Seriphium

Seriphium plumosum L. from southern Africa has been shown to comprise at least seven distinct species based on analysis of morphological, environmental, and multilocus DNA sequence data. On that evidence, Stoebe burchellii Levyns, Stoebe virgata Thunb., and Stoebe vulgaris Levyns are reinstated in Seriphium, and four new species are described, viz., Seriphium alto-argillaceum Z.Shaik, Se. burrowsii Z.Shaik, Se. crypticum Z.Shaik, and Se. dunensis Z.Shaik. Four of these species are endemic to the Cape Floristic Region of South Africa, and one is so far known from a single high-elevation locality. We present a key to species in the Seriphium plumosum complex, and for each species provide a formal description with accompanying images, typification and synonymy, with distribution maps based on verified field-collected and herbarium specimens.

Low-cost IoT-based monitoring system for precision agriculture

The increasing impact of climate change on agriculture necessitates advanced monitoring and management of environmental conditions to ensure sustainable agricultural productivity. This paper introduces a cost-effective, Internet of Things (IoT)-based smart monitoring system designed to provide real-time insights into soil moisture levels and weather conditions across various segments of a single agricultural plot. The system comprises autonomous wireless sensor nodes, a comprehensive weather station, and a centralized base station that collectively capture, process, and relay environmental data to a user-friendly mobile application. Our empirical results demonstrate that this system not only facilitates efficient environmental data monitoring and analysis but also empowers farmers with actionable intelligence for timely decision-making. The proposed model showcases a promising avenue for enhancing agricultural resilience and productivity through technology-driven precision farming.