Search Results (21)

Smart-building systems need reliable sensing, long-term storage, and remote control, but many studies mix up fast network response with slow physical changes in the building. We report measurements from an openHAB deployment at University Lab 1 and University Lab 2 in Greece over 19 months (April 2024–October 2025), with Z-Wave and ZigBee devices and Parquet exports for offline analysis. We treat sensing availability, Message Queuing Telemetry Transport (MQTT) command latency, and heating, ventilation, and air conditioning (HVAC) behavior as separate questions. In March–June 2024, raw record-level temperature field availability was 79.7% and 74.2% at University Lab 1 and University Lab 2, respectively, increasing to 99.7% and 98.5% after the <30 min linear field interpolation used in golden-dataset construction; assessable device-month cadence-normalized reading-count ratios were far lower (7.9% temperature and 13.0% humidity under the 300 s assumption at University Lab 1; humidity 7.8–26.0% across 180–600 s), reflecting heterogeneous archival participation rather than a fully populated expected-cadence denominator over the full archive span. In May–June 2024, 1247 MQTT commands yielded 1246 successful correlated responses; the archived application log contained no repeated correlation identifiers or duplicate response records (broker DUP flags are not archived); median, p90, and p99 latencies were 287 ms, 487 ms, and 1.66 s. These times describe the MQTT–edge-agent–openHAB path only, not physical device action. HVAC figures are illustrative; we do not infer settling times. The results show why availability metrics, archive denominators, and response-time boundaries must be defined separately in smart-building evaluations. Full article

The article presents a method for determining the irrigation requirements of crops based on soil moisture. The proposed approach enables scheduling irrigation at the most appropriate time of day by combining current soil moisture measurements with forecasts of moisture levels for the following day. A narrow Artificial Intelligence (AI) model is developed and applied to the task of 24 h-ahead soil moisture forecasting. Water loss due to excessive irrigation is minimized through precise soil moisture monitoring, postponement or reduction of irrigation in response to measured precipitation, temperature, and wind speed, as well as meteorological forecasts of future rainfall. The proposed irrigation system is suitable for both drip irrigation and central pivot systems. It is built using cost-effective components and incorporates LoRa connectivity, which facilitates integration in remote areas without the need for internet access. Furthermore, the addition of new irrigation zones does not require physical modifications to the central server. Experimental tests demonstrated that the system effectively controls irrigation timing and achieves the desired soil moisture levels with high accuracy, while accounting for additional external factors that influence soil moisture. Full article

Environmental filtering studies have revealed immense oceanic microbial diversity, yet the Southeast Pacific remains comparatively undersampled. We characterize the molecular diversity of phytoplankton across two biogeographic domains with contrasting oceanography—fjords and channels (41–53° S) versus the open Pacific (36–42° S)—where the frequency and intensity of harmful algal blooms (HABs) have increased. Using SSU rRNA metabarcoding, we retrieved community composition and biogeographic patterns for micro-phytoplankton. Diversity signals indicated broadly overlapping communities between domains with subtle shifts along hydrographic and nutrient gradients rather than sharp breaks. Phylogenetic resolution within bloom-forming genera recovered well-supported clades, and multiple ASVs matched historically relevant HAB taxa, including representatives of the Alexandrium complex, Dinophysis, Pseudo-nitzschia, and Karenia. Together, these results suggest that regional environmental filtering acts modestly at the community level while preserving clear signals of taxa of management concern. By providing a regionally resolved, DNA-based baseline for southern Chile’s fjords and adjacent open coast, this study helps fill the molecular diversity gap for the Southeast Pacific and supports improved HAB surveillance and ecosystem forecasting in a climate-sensitive seascape. Full article

The article aims to present a cyber–physical system (CPS) to support the cultivation of aquaculture in a closed aquaponic system using the deep-water culture (DWC) method. The CPS uses precision sensors as TriOxmatic 700 IQ (for dissolved oxygen and water temperature), AmmoLyt Plus 700 IQ (for ammonium), NiCaVis 701 IQ NI (for nitrites and nitrates), SensoLyt^® 700 IQ (for pH), and SL-M5 (for water level). It is built with a Raspberry Pi 4, 8 GB as a server, OpenHAB 3.0 software, and other specialized software for measuring water parameters. Some of the parameters are maintained completely autonomously, while others are indirectly controlled. Basic knowledge of hydroponics and aquaculture is required to set up the system, but day-to-day maintenance can be carried out by employees who receive instructions from the CPS. A method for the physical modification of the fish tank surface by using laser processing is proposed. This results in a change in surface topography (creating diverse microstructure patterns) and its roughness, which is of crucial importance for the bacterial adhesion mechanism. Full article

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m³, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region. Full article

Harmful algal blooms are a global phenomenon that degrade water quality and can result in adverse health impacts to both humans and wildlife. Monitoring algal blooms at scale is extremely difficult due to the lack of coincident data across space and time. Additionally, traditional field collection methods tend to be labor- and cost-prohibitive, resulting in disparate data collection not capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to help alleviate this issue by leveraging large, public, water quality databases coupled with open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usability of four common chlorophyll a algorithms as a proxy for detecting and mapping algal blooms nationwide. Chlorophyll a data were aggregated from spatially diverse sites across the continental United States between 2019 and 2022. Data were aggregated via a field method and matched to coincident Sentinel-2 imagery using k-folds cross-validation to evaluate the performance of the band ratio algorithms at the nationwide scale. Additionally, the dataset was portioned to evaluate the influence of temporal windows and annual consistency on algorithm performance. The 2BDA and the NDCI algorithms were the most viable for broadscale mapping of chlorophyll a, which performed moderately well (R² > 0.5) across the entire continental united states, encompassing highly diverse spatial, temporal, and physical conditions. Algorithms’ performances were consistent across different field methods, temporal windows, and annually. The most compatible field data acquisition method was the chlorophyll a, water, trichromatic method, uncorrected with R² values of 0.63, 0.62, and 0.41 and RMSE values of 15.89, 16.2, and 23.30 for 2BDA, NDCI, and MCI, respectively. These results indicate the feasibility of utilizing band ratio algorithms for broadscale detection and mapping of chlorophyll a as a proxy for HABs, which is especially valuable when coincident data are unavailable or limited. Full article

Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new horizons for HAB forecasting. However, the non-stationary and stochastic process behind algal dynamics monitoring largely limits the prediction performance and the early warning of algal booms. Through an analysis of the published literature, we found that decomposition methods are widely used in time-series analysis for hydrological processes. Predictions of ecological indicators have received less attention due to their inherent fluctuations. This study explores and demonstrates the predictive enhancement for chlorophyll fluorescence data based on the coupling of three decomposition algorithms with conventional deep learning models: the convolutional neural network (CNN) and long short-term memory (LSTM). We found that the decomposition algorithms can successfully capture the time-series patterns of chlorophyll fluorescence concentrations. The results indicate that decomposition-based models can enhance the accuracy of single models in predicting chlorophyll concentrations in terms of the improvement percentages in RMSE (with increases ranging from 25.7% to 71.3%), MAE (ranging from 28.3% to 75.7%), and R² values (increasing ranging from 14.8% to 34.8%). In addition, the comparison experiment for different decomposition methods might suggest the superiority of singular spectral analysis in hourly predictive tasks of chlorophyll fluorescence over the wavelet transform and empirical mode decomposition models. Overall, while decomposition methods come with their respective strengths and weaknesses, they are undeniably efficient in combination with deep learning models in dealing with the high-frequency monitoring of chlorophyll fluorescence data. We also suggest that model developers pay more attention to online data preprocessing and conduct comparative analyses to determine the best model combinations for forecasting algal blooms and water management. Full article

The article presents a Cyber-Physical System (CPS) for intelligent management of a poultry farm for broiler meat production, with a fully autonomous microclimate control. Innovative concepts have been introduced for automated management and changing parameters according to pre-set conditions and schedules, with the possibility that the parameters of the algorithm can be further adjusted by the operator. The proposed CPS provides for high productivity with minimal production waste, at optimized costs and with minimization of human errors. The CPS is built on the basis of cost-oriented components. A Raspberry Pi 4 8 GB is used as the server, and the free open-source software OpenHAB 3.0 is used to optimize the cost of building the system as much as possible. Full article

Harmful algal blooms (HABs) are a serious threat to ecosystems and human health. The accurate prediction of HABs is crucial for their proactive preparation and management. While mechanism-based numerical modeling, such as the Environmental Fluid Dynamics Code (EFDC), has been widely used in the past, the recent development of machine learning technology with data-based processing capabilities has opened up new possibilities for HABs prediction. In this study, we developed and evaluated two types of machine learning-based models for HABs prediction: Gradient Boosting models (XGBoost, LightGBM, CatBoost) and attention-based CNN-LSTM models. We used Bayesian optimization techniques for hyperparameter tuning, and applied bagging and stacking ensemble techniques to obtain the final prediction results. The final prediction result was derived by applying the optimal hyperparameter and bagging and stacking ensemble techniques, and the applicability of prediction to HABs was evaluated. When predicting HABs with an ensemble technique, it is judged that the overall prediction performance can be improved by complementing the advantages of each model and averaging errors such as overfitting of individual models. Our study highlights the potential of machine learning-based models for HABs prediction and emphasizes the need to incorporate the latest technology into this important field. Full article

This paper presents the design and implementation of a versatile IoT testbed utilizing the openHAB platform, along with various wireless interfaces, including Z-Wave, ZigBee, Wi-Fi, 4G-LTE (Long-Term Evolution), and IR (Infrared Radiation), and an array of sensors for motion, temperature, luminance, humidity, vibration, UV (ultraviolet), and energy consumption. First, the testbed architecture, setup, basic testing, and collected data results are described. Then, by showcasing a typical day in the laboratory, we illustrate the testbed’s potential through the collection and analysis of data from multiple sensors. The study also explores the capabilities of the openHAB platform, including its robust persistence layer, event management, real-time monitoring, and customization. The significance of the testbed in enhancing data collection methodologies for energy assets and unlocking new possibilities in the realm of IoT technologies is particularly highlighted. Full article

Toxic and harmful algal blooms (HABs) are a global problem affecting human health, marine ecosystems, and coastal economies, the latter through their impact on aquaculture, fisheries, and tourism. As our knowledge and the techniques to study HABs advance, so do international monitoring efforts, which have led to a large increase in the total number of reported cases. However, in addition to increased detections, environmental factors associated with global change, mainly high nutrient levels and warming temperatures, are responsible for the increased occurrence, persistence, and geographical expansion of HABs. The Chilean Patagonian fjords provide an “open-air laboratory” for the study of climate change, including its impact on the blooms of several toxic microalgal species, which, in recent years, have undergone increases in their geographical range as well as their virulence and recurrence (the species Alexandrium catenella, Pseudochattonella verruculosa, and Heterosigma akashiwo, and others of the genera Dinophysis and Pseudo-nitzschia). Here, we review the evolution of HABs in the Chilean Patagonian fjords, with a focus on the established connections between key features of HABs (expansion, recurrence, and persistence) and their interaction with current and predicted global climate-change-related factors. We conclude that large-scale climatic anomalies such as the lack of rain and heat waves, events intensified by climate change, promote the massive proliferation of these species by creating ideal conditions for their growth and persistence, as they affect water-column stratification, nutrient inputs, and reproductive rates. Full article

A small-scale grid-connected PV system that is easy to install and is inexpensive as a remote monitoring system may cause economic losses if its failure is not found and it is left unattended for a long time. Thus, in this study, we developed a low-cost fault detection remote monitoring system for small-scale grid-connected PV systems. This active monitoring system equipped with a simulation-based fault detection algorithm accurately predicts AC power under normal operating conditions and notifies its failure when the measured power is abnormally low. In order to lower the cost, we used a single board computer (SBC) with edge computing as a data server and designed a monitoring system using openHAB, an open-source software. Additionally, we used the Shewhart control chart as a fault detection criterion and the ratio between the measured and predicted ac power for the normal operation data as an observation. As a result of the verification test for the actual grid-connected PV system, it was confirmed that the developed remote monitoring system was able to accurately identify the system failures in real-time, such as open circuit, short circuit, partial shading, etc. Full article

Measurement of pH in aqueous-organic mixtures with different compositions is of high importance in science and technology, but it is, at the same time, challenging both from a conceptual and practical standpoint. A big part of the difficulty comes from the fundamental incomparability of conventional pH values between solvents (^spH, solvent-specific scales). The recent introduction of the unified pH (pH_abs) concept opens up the possibility of measuring pH, expressed as ${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_2\mathrm{O}}$, in a way that is comparable between solvent, and, thereby, removing the conceptual problem. However, practical issues remain. This work presents the experience of the authors with measuring ${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_2\mathrm{O}}$ values in mixtures of methanol, ethanol, and acetonitrile, with water, but without the presence of buffers or other additives. The aim was to assigned ${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_2\mathrm{O}}$ values to solvent–water mixtures using differential potentiometry and the ‘pH_abs-ladder’ method. Measurements were made of the potential difference between glass electrodes immersed in different solutions, separated by an ionic liquid salt bridge. Data were acquired for a series of solutions of varying solvent content. This work includes experiences related to: a selection of commercial electrodes, purity of starting material, and comparability between laboratories. Ranges of ${\mathrm{pH}}_{\mathrm{abs}}^{{\mathrm{H}}_2\mathrm{O}}$ values for selected compositions of solvent–water mixtures are presented. Full article

Water supply is a primary contributor to the growth and phytonutrient composition in chili peppers. Several physiological stress factors can influence phytonutrients in chili peppers, resulting in their differential synthesis. Maintaining the right and exact amount of water through a drip system can promote an effective fruit set and crop quality. Four pepper cultivars (‘Hetényi Parázs’; HET, ‘Unikal’; UNIK, ‘Unijol’; UNIJ and ‘Habanero’; HAB) were investigated under different water supply treatments: RF or rain-fed, DI or deficit irrigation, and OWS or optimum water supply. The two-year experiment was carried out in May 2018 and 2019 under open field conditions. Physiological parameters (relative chlorophyll content, chlorophyll fluorescence, and canopy temperature) were measured during the growth stage and phytonutrients (vitamin C, capsaicinoids and carotenoids) analyzed using high-performance liquid chromatography (HPLC) at harvest in September. The study revealed that, due to higher precipitation and rainfall interruption, increased water supply affected physiological response and phytonutrients in the cultivars. HAB under OWS had a lower response during the growth period when compared to HET, UNIK, and UNIJ. As water supply increased, measured individual carotenoid concentration increased in some cultivars. On the other hand, as water supply decreased, vitamin C and capsaicinoids concentration increased. Even though cultivars responded to the water supply treatments differently, HET exhibited a more uniform and stable composition in all treatments. Full article